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Tae Hwy Lee

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.

    Mentioned in:

    1. What I Learned Last Week
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-10-13 09:19:00

Working papers

  1. Saman Banafti & Tae-Hwy Lee, 2022. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Papers 2201.06605, arXiv.org, revised Sep 2023.

    Cited by:

    1. Eric Qian, 2023. "Heterogeneity-robust granular instruments," Papers 2304.01273, arXiv.org, revised Jun 2024.

  2. Tae-Hwy Lee & Ekaterina Seregina, 2022. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Papers 2209.01697, arXiv.org, revised Sep 2023.

    Cited by:

    1. Shahnaz Parsaeian, 2024. "Stein-like Common Correlated Effects Estimation under Structural Breaks," Econometrics, MDPI, vol. 12(2), pages 1-23, April.

  3. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal Forecast under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202207, University of Kansas, Department of Economics.

    Cited by:

    1. Shahnaz Parsaeian, 2024. "Stein-like Common Correlated Effects Estimation under Structural Breaks," Econometrics, MDPI, vol. 12(2), pages 1-23, April.

  4. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Forecasting under Structural Breaks Using Improved Weighted Estimation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202212, University of Kansas, Department of Economics.

    Cited by:

    1. Shahnaz Parsaeian, 2024. "Stein-like Common Correlated Effects Estimation under Structural Breaks," Econometrics, MDPI, vol. 12(2), pages 1-23, April.
    2. Zongwu Cai & Gunawan, 2023. "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202310, University of Kansas, Department of Economics, revised Sep 2023.

  5. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2021. "Efficient Combined Estimation under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202107, University of Kansas, Department of Economics.

    Cited by:

    1. Shahnaz Parsaeian, 2024. "Stein-like Common Correlated Effects Estimation under Structural Breaks," Econometrics, MDPI, vol. 12(2), pages 1-23, April.

  6. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Optimal Portfolio Using Factor Graphical Lasso," Papers 2011.00435, arXiv.org, revised Apr 2023.

    Cited by:

    1. Tae-Hwy Lee & Ekaterina Seregina, 2023. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Working Papers 202310, University of California at Riverside, Department of Economics.
    2. Yuanrong Wang & Tomaso Aste, 2021. "Dynamic Portfolio Optimization with Inverse Covariance Clustering," Papers 2112.15499, arXiv.org, revised Jan 2022.
    3. Wang, Yuanrong & Aste, Tomaso, 2023. "Dynamic portfolio optimization with inverse covariance clustering," LSE Research Online Documents on Economics 117701, London School of Economics and Political Science, LSE Library.
    4. Yuanrong Wang & Tomaso Aste, 2022. "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers 2203.03991, arXiv.org.
    5. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).

  7. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2020. "Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility," Working Papers 202015, University of California at Riverside, Department of Economics.

    Cited by:

    1. Subhadeep Mukhopadhyay, 2023. "Abductive Inference and C. S. Peirce: 150 Years Later," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 123-149, March.
    2. Michael William Ashby & Oliver Bruce Linton, 2024. "Do Consumption-Based Asset Pricing Models Explain the Dynamics of Stock Market Returns?," JRFM, MDPI, vol. 17(2), pages 1-41, February.

  8. Tong Fang & Tae-Hwy Lee & Zhi Su, 2020. "Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection," Working Papers 202009, University of California at Riverside, Department of Economics.

    Cited by:

    1. Mawuli Segnon & Rangan Gupta & Bernd Wilfling, 2022. "Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks," Working Papers 202203, University of Pretoria, Department of Economics.
    2. Riso, Luigi & Vacca, Gianmarco, 2024. "Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning," Finance Research Letters, Elsevier, vol. 62(PB).
    3. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2024. "The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model," Papers 2404.01641, arXiv.org.
    4. Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023. "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    5. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    6. Cagliesi, Gabriella & Guidi, Francesco, 2021. "A three-tiered nested analytical approach to financial integration: The case of emerging and frontier equity markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    7. O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024. "Financial Uncertainty and Gold Market Volatility: Evidence from a GARCH-MIDAS Approach with Variable Selection," Working Papers 202441, University of Pretoria, Department of Economics.
    8. Zhao, Xiaojun & Zhang, Na & Zhang, Yali & Xu, Chao & Shang, Pengjian, 2024. "Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
    9. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    10. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    11. Wang, Jiqian & He, Xiaofeng & Ma, Feng & Li, Pan, 2022. "Uncertainty and oil volatility: Evidence from shrinkage method," Resources Policy, Elsevier, vol. 75(C).
    12. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    13. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    14. Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023. "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 358-368.
    15. Li, Dakai, 2024. "Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 115-122.
    16. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
    17. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    18. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    19. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    20. Hengzhen Lu & Qiujin Gao & Ling Xiao & Gurjeet Dhesi, 2024. "Forecasting EUA futures volatility with geopolitical risk: evidence from GARCH-MIDAS models," Review of Managerial Science, Springer, vol. 18(7), pages 1917-1943, July.
    21. Wang, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
    22. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
    23. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    24. Dutta, Anupam & Park, Donghyun & Uddin, Gazi Salah & Kanjilal, Kakali & Ghosh, Sajal, 2024. "Do dirty and clean energy investments react to infectious disease-induced uncertainty?," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    25. Xinyu Wu & Xuebao Yin & Xueting Mei, 2022. "Forecasting the Volatility of European Union Allowance Futures with Climate Policy Uncertainty Using the EGARCH-MIDAS Model," Sustainability, MDPI, vol. 14(7), pages 1-13, April.
    26. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    27. Christian Conrad & Robert F. Engle, 2021. "Modelling Volatility Cycles: The (MF)2 GARCH Model," Working Paper series 21-05, Rimini Centre for Economic Analysis.
    28. Fameliti Stavroula & Skintzi Vasiliki, 2024. "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, vol. 67(5), pages 1967-2007, November.
    29. Caraiani, Petre, 2022. "Using LASSO-family models to estimate the impact of monetary policy on corporate investments," Economics Letters, Elsevier, vol. 210(C).
    30. Liu, Han & Yang, Peng & He, Yongda & Oxley, Les & Guo, Pengwei, 2024. "Exploring the influence of the geopolitical risks on the natural resource price volatility and correlation: Evidence from DCC-MIDAS-X model," Energy Economics, Elsevier, vol. 129(C).
    31. Yao, Yuan & Zhao, Yang & Li, Yan, 2022. "A volatility model based on adaptive expectations: An improvement on the rational expectations model," International Review of Financial Analysis, Elsevier, vol. 82(C).
    32. Ruipeng Liu & Rangan Gupta & Elie Bouri, 2021. "Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective," Working Papers 202178, University of Pretoria, Department of Economics.
    33. Zhang, Li & Wang, Lu & Peng, Lijuan & Luo, Keyu, 2023. "Measuring the response of clean energy stock price volatility to extreme shocks," Renewable Energy, Elsevier, vol. 206(C), pages 1289-1300.
    34. Liu, Feng & Shao, Shuai & Li, Xin & Pan, Na & Qi, Yu, 2023. "Economic policy uncertainty, jump dynamics, and oil price volatility," Energy Economics, Elsevier, vol. 120(C).
    35. Adediran, Idris A. & Swaray, Raymond, 2023. "Carbon trading amidst global uncertainty: The role of policy and geopolitical uncertainty," Economic Modelling, Elsevier, vol. 123(C).
    36. Lu, Xinjie & Ma, Feng & Wang, Tianyang & Wen, Fenghua, 2023. "International stock market volatility: A data-rich environment based on oil shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 184-215.
    37. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    38. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach," Working Papers 202308, University of Pretoria, Department of Economics.
    39. Ghani, Maria & Guo, Qiang & Ma, Feng & Li, Tao, 2022. "Forecasting Pakistan stock market volatility: Evidence from economic variables and the uncertainty index," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 1180-1189.
    40. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
    41. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    42. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    43. Jiqian Wang & Feng Ma & Elie Bouri & Yangli Guo, 2023. "Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 970-988, July.
    44. Tong Fang & Deyu Miao & Zhi Su & Libo Yin, 2023. "Uncertainty‐driven oil volatility risk premium and international stock market volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 872-904, July.
    45. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    46. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    47. Dutta, Anupam & Uddin, Gazi Salah & Sheng, Lin Wen & Park, Donghyun & Zhu, Xuening, 2024. "Volatility dynamics of agricultural futures markets under uncertainties," Energy Economics, Elsevier, vol. 136(C).
    48. Guo, Kun & Liu, Fengqi & Sun, Xiaolei & Zhang, Dayong & Ji, Qiang, 2023. "Predicting natural gas futures’ volatility using climate risks," Finance Research Letters, Elsevier, vol. 55(PA).
    49. Lucas Schneider & Johannes Stübinger, 2020. "Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns," Mathematics, MDPI, vol. 8(9), pages 1-22, September.

  9. Tae-Hwy Lee & Aman Ullah & He Wang, 2018. "The Second-order Asymptotic Properties of Asymmetric Least Squares Estimation," Working Papers 201910, University of California at Riverside, Department of Economics.

    Cited by:

    1. Arnab Bhattacharjee & Tapabrata Maiti, 2019. "P. C. Mahalanobis in the Context of Current Econometrics Research," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 1-11, September.

  10. Tae-Hwy Lee & Eric Hillebrand & Huiyu Huang & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Working Papers 201903, University of California at Riverside, Department of Economics.

    Cited by:

    1. Han, Yang & Jiao, Anqi & Ma, Jun, 2021. "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 95-127.
    2. Rangan Gupta & Sowmya Subramaniam & Elie Bouri & Qiang Ji, 2020. "Infectious Disease-Related Uncertainty and the Safe-Haven Characteristic of US Treasury Securities," Working Papers 202078, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2023. "The role of oil and risk shocks in the high‐frequency movements of the term structure of interest rates: Evidence from the U.S. Treasury market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1845-1857, April.
    4. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    5. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    6. Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021. "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, vol. 42(C).
    7. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.

  11. Tae-Hwy Lee & Bai Huang & Aman Ullah, 2018. "Combined Estimation of Semiparametric Panel Data Models," Working Papers 201915, University of California at Riverside, Department of Economics.

    Cited by:

    1. Günther, Jutta (Ed.) & Wedemeier, Jan (Ed.), 2020. "Struktureller Umbruch durch COVID-19: Implikationen für die Innovationspolitik im Land Bremen," HWWI Policy Papers 128, Hamburg Institute of International Economics (HWWI).
    2. Bagstad, Kenneth J. & Ingram, Jane Carter & Shapiro, Carl D. & La Notte, Alessandra & Maes, Joachim & Vallecillo, Sara & Casey, C. Frank & Glynn, Pierre D. & Heris, Mehdi P. & Johnson, Justin A. & Lau, 2021. "Lessons learned from development of natural capital accounts in the United States and European Union," Ecosystem Services, Elsevier, vol. 52(C).
    3. Hensher, David A., 2021. "The case for negotiated contracts under the transition to a green bus fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 255-269.
    4. Gupta, Joyeeta & Bavinck, Maarten & Ros-Tonen, Mirjam & Asubonteng, Kwabena & Bosch, Hilmer & van Ewijk, Edith & Hordijk, Michaela & Van Leynseele, Yves & Lopes Cardozo, Mieke & Miedema, Esther & Pouw, 2021. "COVID-19, poverty and inclusive development," World Development, Elsevier, vol. 145(C).

  12. Yongmiao Hong & Tae-Hwy Lee & Yuying Sun & Shouyang Wang & Xinyu Zhang, 2017. "Time-varying Model Averaging," Working Papers 202001, University of California at Riverside, Department of Economics.

    Cited by:

    1. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    2. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    3. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    4. Victoria Stack & Lana L. Narine, 2022. "Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus," Sustainability, MDPI, vol. 14(2), pages 1-14, January.
    5. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    6. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2023. "Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202309, University of Kansas, Department of Economics, revised Sep 2023.
    7. Zongwu Cai & Gunawan, 2023. "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202310, University of Kansas, Department of Economics, revised Sep 2023.
    8. Zhang, Xinyu & Liu, Chu-An, 2023. "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, Elsevier, vol. 235(1), pages 280-301.
    9. Yi-Ting Chen & Chu-An Liu, 2021. "Model Averaging for Asymptotically Optimal Combined Forecasts," IEAS Working Paper : academic research 21-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    10. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.

  13. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.

    Cited by:

    1. Chia-Lin Chang & Michael McAleer, 2016. "A Simple Test for Causality in Volatility," Tinbergen Institute Discussion Papers 16-094/III, Tinbergen Institute.
    2. Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud & Shixuan Wang, 2017. "Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality-in-Quantiles," Working Papers 201750, University of Pretoria, Department of Economics.
    3. Daniel Danau, 2018. "Prudence and preference for flexibility gain," Working Papers hal-01806743, HAL.
    4. Corbet, Shaen & Katsiampa, Paraskevi & Lau, Chi Keung Marco, 2020. "Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Zhang, Dongna & Chen, Xihui Haviour & Lau, Chi Keung Marco & Xu, Bing, 2023. "Implications of cryptocurrency energy usage on climate change," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    6. Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud, 2019. "Risk Aversion and Bitcoin Returns in Normal, Bull, and Bear Markets," Working Papers 201927, University of Pretoria, Department of Economics.
    7. Hong Cheng & Yunqing Wang & Yihong Wang & Tinggan Yang, 2022. "Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 719-748, February.
    8. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    9. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    10. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    11. Talbi, Marwa & de Peretti, Christian & Belkacem, Lotfi, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Resources Policy, Elsevier, vol. 66(C).
    12. Roberto Fuentes M. & Irene Crimaldi & Armando Rungi, 2024. "Non-linear dependence and Granger causality: A vine copula approach," Papers 2409.15070, arXiv.org.
    13. Jiang, Cuixia & Li, Yuqian & Xu, Qifa & Liu, Yezheng, 2021. "Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 386-398.
    14. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    15. Hussain Shahzad, Syed Jawad & Raza, Naveed & Shahbaz, Muhammad & Ali, Azwadi, 2017. "Dependence of stock markets with gold and bonds under bullish and bearish market states," Resources Policy, Elsevier, vol. 52(C), pages 308-319.
    16. Akanksha Jalan & Roman Matkovskyy & Larisa Yarovaya, 2021. "“Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic," Post-Print hal-03512893, HAL.
    17. Albulescu, Claudiu Tiberiu & Aubin, Christian & Goyeau, Daniel & Tiwari, Aviral Kumar, 2018. "Extreme co-movements and dependencies among major international exchange rates: A copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 56-69.
    18. Tan Le & Franck Martin & Duc Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Working Papers hal-01806733, HAL.
    19. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    20. Bouri, Elie & Lau, Chi Keung Marco & Lucey, Brian & Roubaud, David, 2019. "Trading volume and the predictability of return and volatility in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 340-346.
    21. Ruan, Qingsong & Zhang, Manqian & Lv, Dayong & Yang, Haiquan, 2018. "SAD and stock returns revisited: Nonlinear analysis based on MF-DCCA and Granger test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1009-1022.
    22. Aharon, David Y. & Demir, Ender & Lau, Chi Keung Marco & Zaremba, Adam, 2022. "Twitter-Based uncertainty and cryptocurrency returns," Research in International Business and Finance, Elsevier, vol. 59(C).
    23. Elie Bouri & Rangan Gupta & Chi keung marco Lau & David Roubaud, 2021. "Risk aversion and Bitcoin returns in extreme quantiles," Economics Bulletin, AccessEcon, vol. 41(3), pages 1374-1386.
    24. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2024. "Temporal networks and financial contagion," Journal of Financial Stability, Elsevier, vol. 71(C).
    25. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    26. Cao, Guangxi & Han, Yan & Li, Qingchen & Xu, Wei, 2017. "Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 119-130.
    27. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    28. Kim, Jong-Min & Lee, Namgil & Hwang, Sun Young, 2020. "A Copula Nonlinear Granger Causality," Economic Modelling, Elsevier, vol. 88(C), pages 420-430.
    29. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
    30. Dastgir, Shabbir & Demir, Ender & Downing, Gareth & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test," Finance Research Letters, Elsevier, vol. 28(C), pages 160-164.
    31. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.

  14. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Forecasting Equity Premium: Global Historical Average versus Local Historical Average and Constraints," Working Papers 201405, University of California at Riverside, Department of Economics.

    Cited by:

    1. Guglielmo Maria Caporale & Luis A. Gil-Alana & Miguel Martin-Valmayor, 2021. "Persistence in the market risk premium: evidence across countries," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(3), pages 413-427, July.
    2. Ren, Yu & Tu, Yundong & Yi, Yanping, 2019. "Balanced predictive regressions," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 118-142.
    3. Lee Tae-Hwy & Wang He & Xi Zhou & Zhang Ru, 2023. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 57-83, January.
    4. Biqing Cai & Jiti Gao, 2017. "A simple nonlinear predictive model for stock returns," Monash Econometrics and Business Statistics Working Papers 18/17, Monash University, Department of Econometrics and Business Statistics.
    5. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    6. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  15. Tae-Hwy Lee & Weiping Yang, 2014. "Money-Income Granger-Causality in Quantiles," Working Papers 201423, University of California at Riverside, Department of Economics, revised Sep 2012.

    Cited by:

    1. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    2. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2023. "Testing Granger Non-Causality in Expectiles," University of East Anglia School of Economics Working Paper Series 2023-02, School of Economics, University of East Anglia, Norwich, UK..
    3. Stephan B. Bruns & Christian Gross & David I. Stern, 2014. "Is There Really Granger Causality between Energy Use and Output?," The Energy Journal, , vol. 35(4), pages 101-134, October.
    4. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    5. Hong Cheng & Yunqing Wang & Yihong Wang & Tinggan Yang, 2022. "Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 719-748, February.
    6. Bertrand Candelon & Sessi Tokpavi, 2016. "A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 240-253, April.
    7. Ahmed, Ali & Granberg, Mark & Troster, Victor & Uddin, Gazi Salah, 2020. "Asymmetric Dynamics between Uncertainty and Unemployment Flows in the United States," LiU Working Papers in Economics 7, Linköping University, Division of Economics, Department of Management and Engineering.
    8. Stephan B. Bruns & David I. Stern, 2015. "Meta-Granger causality testing," CAMA Working Papers 2015-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Marques, André M. & Lima, Gilberto Tadeu, 2022. "Testing for Granger causality in quantiles between the wage share in income and productive capacity utilization," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 290-312.
    10. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable Energy, Oil Prices, and Economic Activity: A Granger-causality in Quantiles Analysis," MPRA Paper 84194, University Library of Munich, Germany, revised 19 Jan 2018.
    11. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  16. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.

    Cited by:

    1. Seul-Ki Park & Ji-Eun Choi & Dong Wan Shin, 2017. "Value at risk forecasting for volatility index," Applied Economics Letters, Taylor & Francis Journals, vol. 24(21), pages 1613-1620, December.
    2. Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
    3. Mario Ivan Contreras-Valdez & Sonal Sahu & José Antonio Núñez-Mora & Roberto Joaquín Santillán-Salgado, 2024. "Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails," Risks, MDPI, vol. 12(3), pages 1-23, March.
    4. Dianliang Deng & Mashfiqul Huq Chowdhury, 2022. "Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions," Stats, MDPI, vol. 5(3), pages 1-23, July.
    5. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    6. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    7. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    8. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.

  17. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.

    Cited by:

    1. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    2. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.

  18. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.

    Cited by:

    1. Zheng Li & Guannan Liu & Qi Li, 2017. "Nonparametric Knn estimation with monotone constraints," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 988-1006, October.
    2. Fu, Zhonghao & Hong, Yongmiao, 2019. "A model-free consistent test for structural change in regression possibly with endogeneity," Journal of Econometrics, Elsevier, vol. 211(1), pages 206-242.
    3. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
    4. Lee Tae-Hwy & Wang He & Xi Zhou & Zhang Ru, 2023. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 57-83, January.
    5. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    6. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.
    7. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  19. Tae-Hwy Lee & Yiyao Wang, 2014. "Asymmetric Loss in the Greenbook and the Survey of Professional Forecasters," Working Papers 201407, University of California at Riverside, Department of Economics.

    Cited by:

    1. Knüppel, Malte & Schultefrankenfeld, Guido, 2018. "Assessing the uncertainty in central banks' inflation outlooks," Discussion Papers 56/2018, Deutsche Bundesbank.
    2. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    3. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    4. Viola Monostoriné Grolmusz, 2023. "Optimal Forecast Combination Under Asymmetric Loss and Regime-Switching," MNB Working Papers 2023/3, Magyar Nemzeti Bank (Central Bank of Hungary).
    5. Jörg Döpke & Ulrich Fritsche & Gabi Waldhof, 2017. "Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey of professional forecasters," Macroeconomics and Finance Series 201701, University of Hamburg, Department of Socioeconomics.
    6. Huh, Sungjun & Kim, Insu, 2020. "Growth forecast revisions over business cycles: Evidence from the Survey of Professional Forecasters," Economics Letters, Elsevier, vol. 196(C).
    7. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    8. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    9. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    10. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    11. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    12. Tsuchiya, Yoichi, 2015. "Herding behavior and loss functions of exchange rate forecasters over interventions and financial crises," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 266-276.
    13. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    14. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    15. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.
    16. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.
    17. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.

  20. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Bent Jesper Christensen & Morten Ørregaard Nielsen & Jie Zhu, 2012. "The impact of financial crises on the risk-return tradeoff and the leverage effect," CREATES Research Papers 2012-19, Department of Economics and Business Economics, Aarhus University.
    2. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Kaufmann, Hendrik & Kruse, Robinson & Sibbertsen, Philipp, 2012. "On tests for linearity against STAR models with deterministic trends," Hannover Economic Papers (HEP) dp-492, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Lee C. Adkins & Melissa S. Waters & R. Carter Hill, 2015. "Collinearity Diagnostics in gretl," Economics Working Paper Series 1506, Oklahoma State University, Department of Economics and Legal Studies in Business.

  21. Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, Department of Economics and Business Economics, Aarhus University.

  22. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2011. "Using the Yield Curve in Forecasting Output Growth and In?flation," CREATES Research Papers 2012-17, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    2. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    3. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    4. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.

  23. Lee, Tae-Hwy & Yang, Weiping, 2007. "Permanent and transitory components of GDP and stock prices: further analysis," UC3M Working papers. Economics we20070525, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Clegg, Matthew & Krauss, Christopher & Rende, Jonas, 2017. "partialCI: An R package for the analysis of partially cointegrated time series," FAU Discussion Papers in Economics 05/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Nazliben, K. Korhan & Rodríguez, Juan Carlos, 2018. "Permanent shocks, signal extraction, and portfolio selection," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 47-68.

  24. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.

    Cited by:

    1. Adrian Cantemir Calin & Tiberiu Diaconescu & Oana – Cristina Popovici, 2014. "Nonlinear Models for Economic Forecasting Applications: An Evolutionary Discussion," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 2(1), pages 42-47, June.

  25. Huiyu Huang & Tae-Hwy Lee, 2006. "To Combine Forecasts or to Combine Information?," Working Papers 200806, University of California at Riverside, Department of Economics, revised Feb 2009.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
    3. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    4. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    5. Qingfeng Liu & Qingsong Yao & Guoqing Zhao, 2020. "Model averaging estimation for conditional volatility models with an application to stock market volatility forecast," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 841-863, August.
    6. Wang, Cindy S.H. & Fan, Rui & Xie, Yiqiang, 2023. "Market systemic risk, predictability and macroeconomics news," Finance Research Letters, Elsevier, vol. 56(C).
    7. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    8. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    9. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Working Papers halshs-01317974, HAL.
    10. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    11. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
    12. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle," Working Paper series 17_12, Rimini Centre for Economic Analysis.
    13. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    14. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    15. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    16. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    17. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    18. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    19. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    20. Jianqing Fan & Yuan Ke & Yuan Liao, 2016. "Augmented Factor Models with Applications to Validating Market Risk Factors and Forecasting Bond Risk Premia," Papers 1603.07041, arXiv.org, revised Sep 2018.
    21. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2015. "On the Forecast Combination Puzzle," Papers 1505.00475, arXiv.org.
    22. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    23. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    24. Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024. "Forecasting realized volatility of crude oil futures prices based on machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1422-1446, August.
    25. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    26. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    27. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.
    28. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    29. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    30. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    31. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    32. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    33. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
    34. Byrne, Joseph & Fu, Rong, 2016. "Stock Return Prediction with Fully Flexible Models and Coefficients," MPRA Paper 75366, University Library of Munich, Germany.
    35. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
    36. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    37. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    38. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Sep 2016.
    39. François Robin, 2018. "Use of Google Trends Data in Banque de France Monthly Retail Trade Surveys," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 35-63.
    40. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    41. Bätje, Fabian & Menkhoff, Lukas, 2016. "Predicting the equity premium via its components," VfS Annual Conference 2016 (Augsburg): Demographic Change 145789, Verein für Socialpolitik / German Economic Association.
    42. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
    43. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    44. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    45. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    46. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    47. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.

  26. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.

    Cited by:

    1. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.

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    3. Nafeesa Yunus, 2016. "Modelling interactions among the housing market and key US sectors," Journal of Property Research, Taylor & Francis Journals, vol. 33(2), pages 121-146, April.
    4. Boubaker, Heni & Zorgati, Mouna Ben Saad & Bannour, Nawres, 2021. "Interdependence between exchange rates: Evidence from multivariate analysis since the financial crisis to the COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 592-608.
    5. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    6. Sascha Keil, 2023. "The challenging estimation of trade elasticities: Tackling the inconclusive Eurozone evidence," The World Economy, Wiley Blackwell, vol. 46(5), pages 1235-1263, May.
    7. Nafeesa Yunus, 2009. "Increasing Convergence Between U.S. and International Securitized Property Markets: Evidence Based on Cointegration Tests," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(3), pages 383-411, September.
    8. Ramya Rajajagadeesan Aroul & Peggy E. Swanson, 2018. "Linkages Between the Foreign Exchange Markets of BRIC Countries—Brazil, Russia, India and China—and the USA," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(3), pages 333-353, December.
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    117. Downes, Andrew S. & Mamingi, Nlandu & Antoine, Rose-Marie Belle, 2000. "Labor Market Regulation and Employment in the Caribbean," IDB Publications (Working Papers) 3261, Inter-American Development Bank.

  30. Gonzalo, J. & Lee, T.H., 1995. "Relative Power of t Type Tests of Stationary and Unit Root Processes," Papers 36, Boston University - Department of Economics.

    Cited by:

    1. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, vol. 2(4), pages 1-14, December.
    2. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    3. Jürgen Wolters & Uwe Hassler, 2006. "Unit root testing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 43-58, March.
    4. Tilak Abeysinghe & Gulasekaran Rajaguru, 2010. "A Gaussian Test for Cointegration," Macroeconomics Working Papers 23040, East Asian Bureau of Economic Research.
    5. González, M., 1997. "Threshold unit root models," DES - Working Papers. Statistics and Econometrics. WS 6214, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
    7. Lim Kai Jie, Shawn & Chadha, Pavneet & Lau, Joshua & Potdar, Nishad, 2012. "Is the Mongolian Equity Market Efficient? Empirical Evidence from Tests of Weak-Form Efficiency," MPRA Paper 41834, University Library of Munich, Germany.
    8. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.

Articles

  1. Tae-Hwy Lee & Ekaterina Seregina, 2024. "Optimal Portfolio Using Factor Graphical Lasso," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 670-695.
    See citations under working paper version above.
  2. Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal forecast under structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 965-987, August.
    See citations under working paper version above.
  3. Lee Tae-Hwy & Mao Millie Yi & Ullah Aman, 2021. "Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 1-19, January.
    See citations under working paper version above.
  4. Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021. "Time-varying model averaging," Journal of Econometrics, Elsevier, vol. 222(2), pages 974-992.
    See citations under working paper version above.
  5. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    See citations under working paper version above.
  6. Huang, Bai & Lee, Tae-Hwy & Ullah, Aman, 2020. "Combined estimation of semiparametric panel data models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 30-45.
    See citations under working paper version above.
  7. Tae-Hwy Lee & Aman Ullah & He Wang, 2019. "The Second-Order Asymptotic Properties of Asymmetric Least Squares Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 201-233, September.
    See citations under working paper version above.
  8. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.

    Cited by:

    1. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon), 2019. "Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations," Applied Energy, Elsevier, vol. 250(C), pages 1402-1417.
    2. Yong Bao & Aman Ullah, 2021. "Analytical Finite Sample Econometrics: From A. L. Nagar to Now," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 17-37, December.

  9. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    See citations under working paper version above.
  10. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2015. "Forecasting Equity Premium: Global Historical Average Versus Local Historical Average and Constraints," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 393-402, July.
    See citations under working paper version above.
  11. Lee, Tae-Hwy & Tu, Yundong & Ullah, Aman, 2014. "Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 196-210.
    See citations under working paper version above.
  12. Lee, Tae-Hwy & Yang, Weiping, 2014. "Granger-causality in quantiles between financial markets: Using copula approach," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 70-78.
    See citations under working paper version above.
  13. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    See citations under working paper version above.
  14. Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, vol. 1(1), pages 1-14, June.
    See citations under working paper version above.
  15. Huiyu Huang & Tae-Hwy Lee, 2010. "To Combine Forecasts or to Combine Information?," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
    See citations under working paper version above.
  16. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.

    Cited by:

    1. Bai, Xiwen & Lam, Jasmine Siu Lee, 2021. "Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    3. Yang, Lu & Cai, Xiao Jing & Li, Mengling & Hamori, Shigeyuki, 2015. "Modeling dependence structures among international stock markets: Evidence from hierarchical Archimedean copulas," Economic Modelling, Elsevier, vol. 51(C), pages 308-314.
    4. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    5. Jammazi, Rania & Tiwari, Aviral Kr. & Ferrer, Román & Moya, Pablo, 2015. "Time-varying dependence between stock and government bond returns: International evidence with dynamic copulas," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 74-93.
    6. Andreas Masuhr, 2017. "Volatility Transmission in Overlapping Trading Zones," CQE Working Papers 6717, Center for Quantitative Economics (CQE), University of Muenster.
    7. Burda Martin & Bélisle Louis, 2019. "Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo," Dependence Modeling, De Gruyter, vol. 7(1), pages 133-149, January.
    8. Herwartz, Helmut & Raters, Fabian H.C., 2015. "Copula-MGARCH with continuous covariance decomposition," Economics Letters, Elsevier, vol. 133(C), pages 73-76.
    9. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    10. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    11. Shi, Peng & Frees, Edward W., 2010. "Long-tail longitudinal modeling of insurance company expenses," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 303-314, December.
    12. Luo, Changqing & Liu, Lan & Wang, Da, 2021. "Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    13. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    14. Jondeau, Eric, 2016. "Asymmetry in tail dependence in equity portfolios," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 351-368.
    15. Chen, Kuan-Ju & Chen, Kuan-Heng, 2016. "Analysis of Energy and Agricultural Commodity Markets with the Policy Mandated: A Vine Copula-based ARMA-EGARCH Model," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236028, Agricultural and Applied Economics Association.
    16. Bodnar, Taras & Hautsch, Nikolaus, 2013. "Copula-based dynamic conditional correlation multiplicative error processes," CFS Working Paper Series 2013/19, Center for Financial Studies (CFS).
    17. Vincenzo Candila, 2021. "Multivariate Analysis of Cryptocurrencies," Econometrics, MDPI, vol. 9(3), pages 1-17, July.
    18. Juwon Seo, 2018. "Randomization Tests for Equality in Dependence Structure," Papers 1811.02105, arXiv.org.
    19. So, Mike K.P. & Chan, Thomas W.C. & Chu, Amanda M.Y., 2022. "Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management," Journal of Econometrics, Elsevier, vol. 227(1), pages 151-167.
    20. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    21. Zongwu Cai & Xian Wang, 2014. "Selection of Mixed Copula Model via Penalized Likelihood," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 788-801, June.
    22. Matthias R. Fengler & Helmut Herwartz & Christian Werner, 2012. "A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew," Journal of Financial Econometrics, Oxford University Press, vol. 10(3), pages 457-493, June.
    23. Hernandez, Manuel A. & Ibarra, Raul & Trupkin, Danilo R., "undated". "How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 124979, International Association of Agricultural Economists.
    24. Shi, Peng, 2012. "Multivariate longitudinal modeling of insurance company expenses," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 204-215.
    25. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    26. Shuangqi Li & Qi‐an Chen, 2021. "Do the Shanghai–Hong Kong & Shenzhen–Hong Kong Stock Connect programs enhance co‐movement between the Mainland Chinese, Hong Kong, and U.S. stock markets?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2871-2890, April.
    27. Monika Papież & Stanisław Wanat & Sławomir Śmiech, 2016. "In Search of Hedges and Safe Havens in Global Financial Markets," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(3), pages 557-574, September.
    28. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2022. "Impacts of COVID-19 outbreak, macroeconomic and financial stress factors on price spillovers among green bond," International Review of Financial Analysis, Elsevier, vol. 81(C).
    29. Heejoon Han, 2016. "Quantile Dependence between Stock Markets and its Application in Volatility Forecasting," Papers 1608.07193, arXiv.org.
    30. Martin Burda & Louis Belisle, 2019. "Copula Multivariate GARCH Model with Constrained Hamiltonian Monte Carlo," Working Papers tecipa-638, University of Toronto, Department of Economics.
    31. Kotkatvuori-Örnberg, Juha, 2016. "Dynamic conditional copula correlation and optimal hedge ratios with currency futures," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 60-69.
    32. Tamakoshi, Go & Hamori, Shigeyuki, 2014. "The conditional dependence structure of insurance sector credit default swap indices," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 122-132.
    33. Krupskii, Pavel & Joe, Harry, 2015. "Structured factor copula models: Theory, inference and computation," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 53-73.
    34. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    35. Xiangdong Long & Liangjun Su & Aman Ullah, 2009. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model Variables with Econometric Applications," Working Papers 200908, University of California at Riverside, Department of Economics, revised Jul 2009.
    36. Dong Hwan Oh & Andrew J. Patton, 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    37. Benjamin Beckers & Helmut Herwartz & Moritz Seidel, 2017. "Risk forecasting in (T)GARCH models with uncorrelated dependent innovations," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 121-137, January.
    38. Power, Gabriel J. & Vedenov, Dmitry V., 2008. "The Shape of the Optimal Hedge Ratio: Modeling Joint Spot-Futures Prices using an Empirical Copula-GARCH Model," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37609, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    39. Graham Bird & Wenti Du & Eric Pentecost & Thomas Willett, 2017. "Safe haven or contagion? The disparate effects of Euro-zone crises on non-Euro-zone neighbours," Applied Economics, Taylor & Francis Journals, vol. 49(59), pages 5895-5904, December.
    40. Miralles-Quirós, José Luis & Miralles-Quirós, María del Mar, 2017. "The Copula ADCC-GARCH model can help PIIGS to fly," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 1-12.
    41. Peng Shi & Wei Zhang, 2011. "A copula regression model for estimating firm efficiency in the insurance industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2271-2287.
    42. Andreas Masuhr, 2019. "Big in Japan: Global Volatility Transmission between Assets and Trading Places," CQE Working Papers 8119, Center for Quantitative Economics (CQE), University of Muenster.
    43. Wang, Zong-Run & Chen, Xiao-Hong & Jin, Yan-Bo & Zhou, Yan-Ju, 2010. "Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH–EVT-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4918-4928.
    44. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    45. Fan, Yanqin & Han, Fang & Park, Hyeonseok, 2023. "Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model," Journal of Econometrics, Elsevier, vol. 237(1).
    46. Chen, Yufeng & Qu, Fang, 2019. "Leverage effect and dynamics correlation between international crude oil and China’s precious metals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    47. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    48. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "The conditional dependence structure between precious metals: a copula-GARCH approach," MPRA Paper 56664, University Library of Munich, Germany.
    49. Tamara Teplova & Mikova Evgeniia & Qaiser Munir & Nataliya Pivnitskaya, 2023. "Black-Litterman model with copula-based views in mean-CVaR portfolio optimization framework with weight constraints," Economic Change and Restructuring, Springer, vol. 56(1), pages 515-535, February.
    50. Cui, Yan & Feng, Yun, 2020. "Composite hedge and utility maximization for optimal futures hedging," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 15-32.
    51. de Oliveira, Felipe A. & Maia, Sinézio F. & de Jesus, Diego P. & Besarria, Cássio da N., 2018. "Which information matters to market risk spreading in Brazil? Volatility transmission modelling using MGARCH-BEKK, DCC, t-Copulas," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 83-100.
    52. Takashi Isogai, 2015. "An Empirical Study of the Dynamic Correlation of Japanese Stock Returns," Bank of Japan Working Paper Series 15-E-7, Bank of Japan.
    53. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    54. Takashi Isogai, 2017. "Analysis of Dynamic Correlation of Japanese Stock Returns with Network Clustering," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 193-220, September.
    55. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    56. Nadine McCloud & Yongmiao Hong, 2011. "Testing The Structure Of Conditional Correlations In Multivariate Garch Models: A Generalized Cross‐Spectrum Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(4), pages 991-1037, November.
    57. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
    58. Kenichiro Shiraya & Kanji Suzuki & Tomohisa Yamakami, 2024. "New approaches of the DCC-GARCH residual: Application to foreign exchange rates," Papers 2411.08246, arXiv.org.
    59. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    60. Felipe de Oliveira & Sinézio Fernandes Maia & Diego Pita de Jesus, 2017. "Which information matters to Market risk spreading in Brazil? Volatility transmission modeling using MGARH-BEKK, DCC, t-COPULAS," EcoMod2017 10378, EcoMod.
    61. Bai, Xiwen & Lam, Jasmine Siu Lee, 2019. "A copula-GARCH approach for analyzing dynamic conditional dependency structure between liquefied petroleum gas freight rate, product price arbitrage and crude oil price," Energy Economics, Elsevier, vol. 78(C), pages 412-427.
    62. Okhrin, Ostap, 2010. "Fitting high-dimensional copulae to data," SFB 649 Discussion Papers 2010-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  17. Jesus Gonzalo & Tae-Hwy Lee & Weiping Yang, 2008. "Permanent and transitory components of GDP and stock prices: further analysis," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 1(1), pages 105-120.
    See citations under working paper version above.
  18. Gloria González-Rivera & Tae-Hwy Lee & Santosh Mishra, 2008. "Jumps in cross-sectional rank and expected returns: a mixture model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 585-606.

    Cited by:

    1. Liang-Ching Lin & Li-Hsien Sun, 2019. "Modeling financial interval time series," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-20, February.
    2. Gloria Gonzalez-Rivera & Javier Arroyo & Carlos Mate, 2011. "Forecasting with Interval and Histogram Data. Some Financial Applications," Working Papers 201438, University of California at Riverside, Department of Economics.
    3. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    4. Arroyo, Javier & Maté, Carlos, 2009. "Forecasting histogram time series with k-nearest neighbours methods," International Journal of Forecasting, Elsevier, vol. 25(1), pages 192-207.

  19. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.

    Cited by:

    1. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    2. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Post-Print hal-00732675, HAL.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. John M. Maheu & Thomas H. McCurdy, 2009. "Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?," Working Paper series 19_09, Rimini Centre for Economic Analysis.
    5. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring Output Gap Uncertainty," Birkbeck Working Papers in Economics and Finance 0909, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    7. Dijk, D. van & Diks, C.G.H. & Panchenko, V., 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," CeNDEF Working Papers 08-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    8. Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier, 2011. "Multivariate semi-nonparametric distributions with dynamic conditional correlations," International Journal of Forecasting, Elsevier, vol. 27(2), pages 347-364.
    9. Cheng, Xixin & Li, W.K. & Yu, Philip L.H. & Zhou, Xuan & Wang, Chao & Lo, P.H., 2011. "Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2590-2604, September.
    10. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
    11. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    12. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    13. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print hal-00834423, HAL.
    14. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
    15. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    16. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    17. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  20. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Yoldas, Emre, 2007. "Optimality of the RiskMetrics VaR model," Finance Research Letters, Elsevier, vol. 4(3), pages 137-145, September.

    Cited by:

    1. Marco Taboga, 2014. "The Riskiness of Corporate Bonds," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 693-713, June.
    2. Krastyu Georgiev & Young Kim & Stoyan Stoyanov, 2015. "Periodic portfolio revision with transaction costs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 337-359, June.
    3. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    4. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.

  21. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.

    Cited by:

    1. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    2. Jian Zhou, 2013. "Extreme risk spillover among international REIT markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(2), pages 91-103, January.
    3. Andriosopoulos, Kostas & Doumpos, Michael & Papapostolou, Nikos C. & Pouliasis, Panos K., 2013. "Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 16-34.
    4. Tchamyou, Vanessa & Asongu, Simplice, 2017. "Conditional Market Timing in the Mutual Fund Industry," MPRA Paper 82633, University Library of Munich, Germany.
    5. Christina Ziegler, 2009. "Testing Predicitive Ability of Business Cycle Indicators for the Euro Area," ifo Working Paper Series 69, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    7. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    8. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    9. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
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  22. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.

    Cited by:

    1. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    2. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    3. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
    4. David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
    5. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    6. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    7. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CARF F-Series CARF-F-189, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    8. Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
    9. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    10. Lechner, Michael & Melly, Blaise, 2007. "Earnings Effects of Training Programs," CEPR Discussion Papers 6400, C.E.P.R. Discussion Papers.
    11. Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024. "Forecasting realized volatility of crude oil futures prices based on machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1422-1446, August.
    12. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    13. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    14. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).
    15. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    16. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    17. Marañon, Matias & Kumral, Mustafa, 2021. "Empirical analysis of Chile's copper boom and the Dutch Disease through causality and cointegration tests," Resources Policy, Elsevier, vol. 70(C).
    18. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    19. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
    20. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    21. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    22. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    23. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.
    24. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.

  23. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.

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    1. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    2. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    3. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    4. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    5. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
    6. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
    7. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    8. Wei Kuang, 2022. "Oil tail-risk forecasts: from financial crisis to COVID-19," Risk Management, Palgrave Macmillan, vol. 24(4), pages 420-460, December.
    9. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    10. Arturo Leccadito & Alessandro Staino & Pietro Toscano, 2024. "A novel robust method for estimating the covariance matrix of financial returns with applications to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
    11. Royer, Julien, 2023. "Conditional asymmetry in Power ARCH(∞) models," Journal of Econometrics, Elsevier, vol. 234(1), pages 178-204.
    12. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    13. Luca Vincenzo Ballestra & Enzo D’Innocenzo & Andrea Guizzardi, 2024. "Score-Driven Modeling with Jumps: An Application to S&P500 Returns and Options," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 375-406.
    14. Pouliasis, Panos K. & Papapostolou, Nikos C. & Kyriakou, Ioannis & Visvikis, Ilias D., 2018. "Shipping equity risk behavior and portfolio management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 178-200.
    15. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models," FinMaP-Working Papers 46, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    16. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    17. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    18. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
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    Cited by:

    1. Chen, Min & Zhu, Ke, 2013. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," MPRA Paper 50487, University Library of Munich, Germany.
    2. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    3. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    4. Yi-Ting Chen, 2008. "A unified approach to standardized-residuals-based correlation tests for GARCH-type models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 111-133.
    5. Zacharias Psaradakis & Marián Vávra, 2015. "Portmanteau Tests for Linearity of Stationary Time Series," Birkbeck Working Papers in Economics and Finance 1514, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Philippe Lambert & Sébastien Laurent, 2008. "Testing Conditional Dynamics in Asymmetry. A Residual-Based Approach," Working Papers ECARES 2008_009, ULB -- Universite Libre de Bruxelles.
    7. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.
    8. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    9. Lambert, Philippe & Laurent, Sébastien & Veredas, David, 2012. "Testing conditional asymmetry: A residual-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1229-1247.
    10. Gloria González-Rivera & Tae-Hwy Lee & Santosh Mishra, 2008. "Jumps in cross-sectional rank and expected returns: a mixture model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 585-606.
    11. Escanciano, J. Carlos, 2006. "Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 531-541, June.
    12. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    13. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
    14. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, September.
    15. Juan Carlos Escanciano, 2006. "Joint Diagnostic Tests for Conditional Mean and Variance Specifications," Faculty Working Papers 02/06, School of Economics and Business Administration, University of Navarra.
    16. M. Dolores Jiménez-Gamero & Sangyeol Lee & Simos G. Meintanis, 2020. "Goodness-of-fit tests for parametric specifications of conditionally heteroscedastic models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 682-703, September.
    17. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    18. Chen, Min & Zhu, Ke, 2015. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," Journal of Econometrics, Elsevier, vol. 189(2), pages 313-320.
    19. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    20. Gao, Jiti & Kim, Nam Hyun & Saart, Patrick W., 2015. "A misspecification test for multiplicative error models of non-negative time series processes," Journal of Econometrics, Elsevier, vol. 189(2), pages 346-359.
    21. Markus J. Fülle & Helmut Herwartz, 2024. "Predicting tail risks by a Markov switching MGARCH model with varying copula regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2163-2186, September.
    22. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    23. Patrick W Saart & Jiti Gao & Nam Hyun Kim, 2014. "Econometric Time Series Specification Testing in a Class of Multiplicative Error Models," Monash Econometrics and Business Statistics Working Papers 1/14, Monash University, Department of Econometrics and Business Statistics.
    24. Meitz, Mika & Terasvirta, Timo, 2006. "Evaluating Models of Autoregressive Conditional Duration," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 104-124, January.
    25. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    26. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  25. Yongmiao Hong & Tae-Hwy Lee, 2003. "Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1048-1062, November.

    Cited by:

    1. Carlo Altavilla & Paul De Grauwe, 2006. "Forecasting and Combining Competing Models of Exchange Rate Determination," CESifo Working Paper Series 1747, CESifo.
    2. García Ruiz Reyna Susana & Cruz Aké Salvador & Venegas Martínez Francisco, 2014. "Una medida de eficiencia de mercado: Un enfoque de teoría de la información," Contaduría y Administración, Accounting and Management, vol. 59(4), pages 137-166, octubre-d.
    3. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    4. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.
    5. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2014. "Forecasting the Oil-gasoline Price Relationship: Should We Care about the Rockets and the Feathers?," Working Papers 2014.21, Fondazione Eni Enrico Mattei.
    6. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    7. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    8. Roch, Oriol, 2013. "Histogram-based prediction of directional price relatives," Finance Research Letters, Elsevier, vol. 10(3), pages 110-115.
    9. Peter C.B. Phillips & Sainan Jin, 2013. "Testing the Martingale Hypothesis," Cowles Foundation Discussion Papers 1912, Cowles Foundation for Research in Economics, Yale University.
    10. Pippenger, John, 2012. "The Fragility of Overshooting," University of California at Santa Barbara, Economics Working Paper Series qt4rd5j98c, Department of Economics, UC Santa Barbara.
    11. Cai, Zongwu & Li, Qi, 2008. "Nonparametric Estimation Of Varying Coefficient Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1321-1342, October.
    12. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    13. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    14. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    15. Altavilla, Carlo & Ciccarelli, Matteo, 2010. "Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts," Economic Modelling, Elsevier, vol. 27(1), pages 237-253, January.
    16. Yang, Jian & Su, Xiaojing & Kolari, James W., 2008. "Do Euro exchange rates follow a martingale? Some out-of-sample evidence," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 729-740, May.
    17. Elaine Y. L. Loh, 2007. "An alternative test for weak form efficiency based on technical analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 17(12), pages 1003-1012.
    18. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    19. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    20. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    21. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    22. Todd E. Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Papers (Old Series) 1121, Federal Reserve Bank of Cleveland.
    23. Pippenger, John, 2008. "Freely Floating Exchange Rates Do Not Systematically Overshoot," University of California at Santa Barbara, Economics Working Paper Series qt97m8z6hw, Department of Economics, UC Santa Barbara.
    24. Xiaoping Xu & Zongwu Cai, 2013. "Nonparametric Quantile Estimations For Dynamic Smooth Coefficient Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    25. Cai, Zongwu, 2003. "Trending Time-Varying Coefficient Models With Serially Correlated Errors," SFB 373 Discussion Papers 2003,7, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    26. Amélie Charles & Olivier Darné & Jae H. Kim, 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Post-Print hal-00958288, HAL.
    27. Sarantis, Nicholas, 2006. "On the short-term predictability of exchange rates: A BVAR time-varying parameters approach," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2257-2279, August.
    28. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2023. "Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202309, University of Kansas, Department of Economics, revised Sep 2023.
    29. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    30. McCracken, Michael W., 2004. "Parameter estimation and tests of equal forecast accuracy between non-nested models," International Journal of Forecasting, Elsevier, vol. 20(3), pages 503-514.
    31. Shintani, Mototsugu, 2005. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 517-538, June.
    32. Adeyeye Patrick Olufemi & Aluko Olufemi Adewale & Migiro Stephen Oseko, 2017. "Efficiency of Foreign Exchange Markets in Sub-Saharan Africa in the Presence of Structural Break: A Linear and Non-Linear Testing Approach," Journal of Economics and Behavioral Studies, AMH International, vol. 9(4), pages 122-131.
    33. Massimo Guidolin & Francesco Chincoli, 2017. "Linear and Nonlinear Predictability in Investment Style Factors: Multivariate Evidence," BAFFI CAREFIN Working Papers 1754, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    34. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2011. "Testing the martingale difference hypothesis in CO2 emission allowances," Economic Modelling, Elsevier, vol. 28(1), pages 27-35.
    35. Al-Khazali, Osamah M. & Pyun, Chong Soo & Kim, Daewon, 2012. "Are exchange rate movements predictable in Asia-Pacific markets? Evidence of random walk and martingale difference processes," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 221-231.
    36. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    37. Huang, Henry H. & Wang, Kent & Wang, Zhanglong, 2016. "A test of efficiency for the S&P 500 index option market using the generalized spectrum method," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 52-70.
    38. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 176, WU Vienna University of Economics and Business.
    39. Matteo Ciccarelli & Carlo Altavilla, 2007. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro area," 2007 Meeting Papers 315, Society for Economic Dynamics.
    40. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
    41. Zongwu Cai, 2013. "Functional Coefficient Models for Economic and Financial Data," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    42. Marcus F. da Silva & Eder Johnson de Area Leão Pereira & Idaraí Santos de Santana & José Garcia Vivas Miranda, 2013. "Pattern of fluctuations in the exchange rate change from fixed to floating, in Brazil, Argentina and Mexico," Economics Bulletin, AccessEcon, vol. 33(2), pages 1547-1555.
    43. Guo, Hui & Savickas, Robert, 2008. "Forecasting foreign exchange rates using idiosyncratic volatility," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1322-1332, July.
    44. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    45. Irina Murtazashvili & Di Liu & Artem Prokhorov, 2015. "Two-sample nonparametric estimation of intergenerational income mobility in the United States and Sweden," Canadian Journal of Economics, Canadian Economics Association, vol. 48(5), pages 1733-1761, December.
    46. McPherson, Matthew Q. & Palardy, Joseph, 2007. "Are international stock returns predictable?: An examination of linear and non-linear predictability using generalized spectral tests," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(5), pages 452-464, December.
    47. Manish Kumar, 2010. "Modelling Exchange Rate Returns Using Non-linear Models," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(1), pages 101-125, January.
    48. Haiqiang Chen & Terence Chong & Jushan Bai, 2012. "Theory and Applications of TAR Model with Two Threshold Variables," Econometric Reviews, Taylor & Francis Journals, vol. 31(2), pages 142-170.
    49. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
    50. Moura, Marcelo L. & Lima, Adauto R. S., 2007. "Empirical exchange rate models fit: Evidence from the Brazilian economy," Insper Working Papers wpe_87, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    51. Manish KUMAR, 2009. "Exploiting The Information Of Stock Market To Forecast Exchange Rate Movements," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice (1954-2015), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 56, pages 563-575, November.
    52. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    53. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Miranda, José G.V. & García-Rubio, Raquel, 2013. "How fast do stock prices adjust to market efficiency? Evidence from a detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1631-1637.
    54. Cai, Zongwu & Das, Mitali & Xiong, Huaiyu & Wu, Xizhi, 2006. "Functional coefficient instrumental variables models," Journal of Econometrics, Elsevier, vol. 133(1), pages 207-241, July.
    55. Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
    56. Moura, Marcelo L. , & Lima, Adauto R. S. & Mendonça, Rodrigo M., 2008. "Exchange Rate and Fundamentals: The Case of Brazil," Insper Working Papers wpe_114, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    57. Kiani, Khurshid M., 2013. "Can signal extraction help predict risk premia in foreign exchange rates," Economic Modelling, Elsevier, vol. 33(C), pages 926-939.
    58. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    59. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    60. Zongwu Cai & Henong Li, 2013. "Convergency and Divergency of Functional Coefficient Weak Instrumental Variables Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    61. Rahimi, Fatemeh & Mousavian Anaraki, Seyed Alireza, 2020. "Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 423-444, October.
    62. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    63. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    64. Cai, Zongwu & Hong, Yongmiao, 2003. "Nonparametric Methods in Continuous-Time Finance: A Selective Review," SFB 373 Discussion Papers 2003,15, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    65. Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
    66. Juan Carlos Escanciano, 2005. "On the Asymptotic Power Properties of Specification Tests for Dynamic Parametric Regressions," Faculty Working Papers 07/05, School of Economics and Business Administration, University of Navarra.

  26. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

    Cited by:

    1. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. Stephanos Papadamou & George Stephanides, 2004. "Evaluating the style-based risk model for equity mutual funds investing in Europe," Applied Financial Economics, Taylor & Francis Journals, vol. 14(10), pages 751-760.
    3. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.

  27. Lee Tae-Hwy, 2001. "Neural Network Test and Nonparametric Kernel Test for Neglected Nonlinearity in Regression Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(4), pages 1-15, January.

    Cited by:

    1. Mototsugu Shintani, 2004. "A Dynamic Factor Approach to Nonlinear Stability Analysis," Vanderbilt University Department of Economics Working Papers 0418, Vanderbilt University Department of Economics.
    2. Lee Tae-Hwy & Xi Zhou & Zhang Ru, 2013. "Testing for Neglected Nonlinearity Using Artificial Neural Networks with Many Randomized Hidden Unit Activations," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 61-68, January.
    3. Shintani, Mototsugu, 2005. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 517-538, June.
    4. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    5. Bracewell Paul J & Farhadieh Farinaz & Jowett Clint A & Forbes Don G. R. & Meyer Denny H, 2009. "Was Bradman Denied His Prime?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(4), pages 1-26, October.

  28. Jesus Gonzalo & Tae-Hwy Lee, 2000. "On the robustness of cointegration tests when series are fractionally intergrated," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(7), pages 821-827.
    See citations under working paper version above.
  29. Clive Granger & Tae-Hwy Lee, 1999. "The effect of aggregation on nonlinearity," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 259-269.

    Cited by:

    1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
    2. Maria Nikoloudaki & Dikaios Tserkezos, 2008. "Temporal Aggregation Effects in Choosing the Optimal Lag Order in Stable ARMA Models: Some Monte Carlo Results," Working Papers 0822, University of Crete, Department of Economics.
    3. Dick van Dijk & Dennis Fok & Philip Hans Franses, 2005. "A multi-level panel STAR model for US manufacturing sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 811-827.
    4. Hashem M. Pesaran & Alexander Chudik, 2011. "Aggregation in Large Dynamic Panels," CESifo Working Paper Series 3346, CESifo.
    5. Valerie Grossman & Enrique Martínez García & Efthymios Pavlidis, 2017. "Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices," Globalization Institute Working Papers 325, Federal Reserve Bank of Dallas.
    6. Fok, Dennis & van Dijk, Dick & Franses, Philip Hans, 2005. "Forecasting aggregates using panels of nonlinear time series," International Journal of Forecasting, Elsevier, vol. 21(4), pages 785-794.
    7. Sarno, Lucio & Taylor, Mark P. & Chowdhury, Ibrahim, 2004. "Nonlinear dynamics in deviations from the law of one price: a broad-based empirical study," Journal of International Money and Finance, Elsevier, vol. 23(1), pages 1-25, February.
    8. Dramane Coulibaly & Hubert Kempf, 2017. "Inflation Targeting and the Forward Bias Puzzle in Emerging Countries," EconomiX Working Papers 2017-12, University of Paris Nanterre, EconomiX.
    9. Denny Meyer & Rob J. Hyndman, 2005. "Rating Forecasts for Television Programs," Monash Econometrics and Business Statistics Working Papers 1/05, Monash University, Department of Econometrics and Business Statistics.
    10. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    11. Silva Lopes, Artur C. & Florin Zsurkis, Gabriel, 2015. "Revisiting non-linearities in business cycles around the world," MPRA Paper 65668, University Library of Munich, Germany.
    12. Lucio Sarno & Giorgio Valente & Hyginus Leon, 2006. "Nonlinearity in Deviations from Uncovered Interest Parity: An Explanation of the Forward Bias Puzzle," Review of Finance, European Finance Association, vol. 10(3), pages 443-482, September.
    13. Silva Lopes, Artur C. & Florin Zsurkis, Gabriel, 2017. "Are linear models really unuseful to describe business cycle data?," MPRA Paper 79413, University Library of Munich, Germany.
    14. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    15. Lopes, Artur Silva & Zsurkis, Gabriel Florin, 2017. "Are linear models really unuseful to describe business cycle data?," Economics Discussion Papers 2017-5, Kiel Institute for the World Economy (IfW Kiel).
    16. Psaradakis Zacharias, 2000. "p-Value Adjustments for Multiple Tests for Nonlinearity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(3), pages 1-8, October.
    17. Ivan Paya & David A. Peel, 2011. "Systematic sampling of nonlinear models: Evidence on speed of adjustment in index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(2), pages 192-203, February.
    18. Maria Simona Andreano & Giovanni Savio, 2002. "Further evidence on business cycle asymmetries in G7 countries," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 895-904.
    19. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.

  30. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    See citations under working paper version above.
  31. Lee, Tae-Hwy, 1996. "Stock Adjustment for Multicointegrated Series," Empirical Economics, Springer, vol. 21(4), pages 633-639.

    Cited by:

    1. Siliverstovs, Boriss & Engsted, Tom & Haldrup, Niels, 2002. "Long-Run Forecasting in Multicointegrated Systems," Finance Working Papers 02-14, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. Scheiblecker, Marcus, 2013. "Between cointegration and multicointegration: Modelling time series dynamics by cumulative error correction models," Economic Modelling, Elsevier, vol. 31(C), pages 511-517.
    3. Boriss Siliverstovs, "undated". "Multicointegration in US consumption data," Economics Working Papers 2001-6, Department of Economics and Business Economics, Aarhus University.
    4. Lunsford, Kurt G., 2015. "Forecasting residential investment in the United States," International Journal of Forecasting, Elsevier, vol. 31(2), pages 276-285.
    5. Peter C.B. Phillips & Igor Kheifets, 2021. "On Multicointegration," Cowles Foundation Discussion Papers 2306, Cowles Foundation for Research in Economics, Yale University.
    6. Demiralp, Berna & Gantt, Bonnie B. & Selover, David D., 2011. "Modeling unemployment as an inventory: A multicointegration approach," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 724-737.
    7. Phillips, Peter C.B. & Kheifets, Igor L., 2024. "High-dimensional IV cointegration estimation and inference," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Hassler, Uwe, 2007. "Multicointegration under measurement errors," Economics Letters, Elsevier, vol. 96(1), pages 38-44, July.
    9. Hassler Uwe, 2001. "Wealth and Consumption. A Multicointegrated Model for the Unified Germany / Vermögen und Konsum. Ein multikointegriertes Modell für das vereinigte Deutschland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 221(1), pages 32-44, February.

  32. Lee, Tae-Hwy & Tse, Yiuman, 1996. "Cointegration tests with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 73(2), pages 401-410, August.

    Cited by:

    1. Bosupeng, Mpho & Biza-Khupe, Simangaliso, 2015. "The Impact of Money Supply Volatility on the Fisher Effect –A Botswana Empirical Perspective," MPRA Paper 77920, University Library of Munich, Germany, revised 2015.
    2. Jesús Otero & Costas Milas, 2001. "Modelling Official And Parallel Exchange Rates In Colombia Under Alternative Regimes: A Non-Linear Approach," CeNDEF Workshop Papers, January 2001 PO2, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. Alizadeh, Amir H. & Huang, Chih-Yueh & van Dellen, Stefan, 2015. "A regime switching approach for hedging tanker shipping freight rates," Energy Economics, Elsevier, vol. 49(C), pages 44-59.
    4. Panayiotis Diamantis & Dimitris Georgoutsos & George Kouretas, "undated". "The Monetary Approach To The Exchange Rate: Long-Run Relationships, Identification And Temporal Stability," Working Papers 9507, University of Crete, Department of Economics.
    5. Jian Yang & Cheng Hsiao & Qi Li & Zijun Wang, 2005. "The Emerging Market Crisis and Stock Market Linkages: Further Evidence," IEPR Working Papers 05.27, Institute of Economic Policy Research (IEPR).
    6. Michael S. Haigh & David A. Bessler, 2004. "Causality and Price Discovery: An Application of Directed Acyclic Graphs," The Journal of Business, University of Chicago Press, vol. 77(4), pages 1099-1121, October.
    7. Paolo Mazza & Mikael Petitjean, 2015. "How integrated is the European carbon derivatives market?," Post-Print hal-01526028, HAL.
    8. George Kouretas & Leonidas Zarangas, "undated". "Black and Official Exchange Rates in Greece: An Analysis of their long-run dynamics," Working Papers 9902, University of Crete, Department of Economics.
    9. Caporale, Guglielmo Maria & Girardi, Alessandro, 2013. "Price discovery and trade fragmentation in a multi-market environment: Evidence from the MTS system," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 227-240.
    10. Chow, Sheung Chi & Vieito, João Paulo & Wong, Wing-Keung, 2018. "Do both demand-following and supply-leading theories hold true in developing countries?," MPRA Paper 87641, University Library of Munich, Germany.
    11. Dimpfl, Thomas, 2014. "A note on cointegration of international stock market indices," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 10-16.
    12. Kumudini R. Ganegodage & Alicia N. Rambaldi, 2012. "Economic Consequences of War: Evidence from Sri Lanka," Discussion Papers Series 453, School of Economics, University of Queensland, Australia.
    13. Eleni Constantinou & Avo Kazandjian & George Kouretas & Vera Tahmazian, 2005. "Common Stochastic Trends among the Cyprus Stock Exchange and the ASE, LSE and NYSE," Working Papers 0520, University of Crete, Department of Economics.
    14. Kühl, Michael, 2007. "Cointegration in the foreign exchange market and market efficiency since the introduction of the Euro: Evidence based on bivariate cointegration analyses," University of Göttingen Working Papers in Economics 68, University of Goettingen, Department of Economics.
    15. Strauss, Jack & Yigit, Taner, 2001. "Present value model, heteroscedasticity and parameter stability tests," Economics Letters, Elsevier, vol. 73(3), pages 375-378, December.
    16. Diamandis, Panayiotis F., 2009. "International stock market linkages: Evidence from Latin America," Global Finance Journal, Elsevier, vol. 20(1), pages 13-30.
    17. Mezgebo, Taddese, 2009. "A multivariate approach for identification of optimal locations with in Ethiopia’s wheat market to tackle soaring inflation on food price," MPRA Paper 18663, University Library of Munich, Germany.
    18. Dimitris Georgoutsos & George Kouretas, 2001. "Common Stochastic Trends In International Stock Markets: Testing In An Integrated Framework," Working Papers 0104, University of Crete, Department of Economics.
    19. Giuseppe Cavaliere & Anders Rahbek & A.M.Robert Taylor, 2009. "Co-integration Rank Testing under Conditional Heteroskedasticity," CREATES Research Papers 2009-22, Department of Economics and Business Economics, Aarhus University.
    20. Díaz-Emparanza Herrero, Ignacio & Miranda Espinosa, Alexandra, 2000. "Analysis of the relationship between International Immigration and Unemployement," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    21. Brian M Lucey & Cal Muckley, 2011. "Robust Global Stock Market Interdependencies," The Institute for International Integration Studies Discussion Paper Series iiisdp353, IIIS.
    22. Klaus Grobys, 2015. "Size distortions of the wild bootstrapped HCCME-based LM test for serial correlation in the presence of asymmetric conditional heteroskedasticity," Empirical Economics, Springer, vol. 48(3), pages 1189-1202, May.
    23. Christos Kollias & Charis Naxakisb & Leonidas Zarangasb, 2004. "Defence Spending and Growth in Cyprus: A Causal Analysis," Defence and Peace Economics, Taylor & Francis Journals, vol. 15(3), pages 299-307.
    24. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, January.
    25. Woo, Kai-Yin, 1999. "Cointegration analysis of the intensity of the ERM currencies under the European Monetary System," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 393-405, November.
    26. Tse, Yiuman & Booth, G. Geoffrey, 1997. "Information shares in international oil futures markets," International Review of Economics & Finance, Elsevier, vol. 6(1), pages 49-56.
    27. C. Kailash P. & К. Прадхам Ч., 2017. "Движение цен на спотовых и фьючерсных рынках: Подтверждение индексами S&P CNX NIFTY // Price movements in futures and spot markets: Evidence from the S&P CNX Nifty Index," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 5(1), pages 32-41.
    28. Thuraisamy, Kannan & Gannon, Gerard, 2012. "Modelling the Sovereign Linkages of Key Latin American Economies," Working Papers fe_2012_03, Deakin University, Department of Economics.
    29. Y Hsing, 2004. "Impacts of Dollar Depreciation and Low Deposit Rates on the US Economy," Economic Issues Journal Articles, Economic Issues, vol. 9(1), pages 1-14, March.
    30. Bee Wah Tan & Chor Foon Tang, 2016. "Examining the Causal Linkages among Domestic Investment, FDI, Trade, Interest Rate and Economic Growth in ASEAN-5 Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 214-220.
    31. Geoffrey Booth, G. & Chowdhury, Mustafa & Martikainen, Teppo, 1996. "Common volatility in major stock index futures markets," European Journal of Operational Research, Elsevier, vol. 95(3), pages 623-630, December.
    32. Apostolos G. Christopoulos & Spyros Papathanasiou & Petros Kalantonis & Andreas Chouliaras & Savvas Katsikides, 2014. "An Investigation of Cointegration and Casualty Relationships between the PIIGS’ Stock Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 109-123.
    33. Yuanlong Ge & Holly H. Wang & Sung K. Ahn, 2010. "Cotton market integration and the impact of China's new exchange rate regime," Agricultural Economics, International Association of Agricultural Economists, vol. 41(5), pages 443-451, September.
    34. William A. Barnett & Barry E. Jones & Milka Kirova & Travis Nesmith & Meenakshi Pasupathy, 2004. "The Nonlinear Skeletons in the Closet," Econometrics 0405003, University Library of Munich, Germany.
    35. Manolis Kavussanos & Nikos Nomikos, 2003. "Price Discovery, Causality and Forecasting in the Freight Futures Market," Review of Derivatives Research, Springer, vol. 6(3), pages 203-230, October.
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    Cited by:

    1. Geoffrey Booth, G. & Ciner, Cetin, 1997. "International transmission on information in corn futures markets," Journal of Multinational Financial Management, Elsevier, vol. 7(3), pages 175-187, October.
    2. John M. Sequeira & Pang Chia Chiat & Michael McAleer, 2003. "Volatility Models of Currency Futures in Developed and Emerging Markets," CIRJE F-Series CIRJE-F-210, CIRJE, Faculty of Economics, University of Tokyo.
    3. Kenneth Yung & Yen-Chih Liu, 2009. "Implications of futures trading volume: Hedgers versus speculators," Journal of Asset Management, Palgrave Macmillan, vol. 10(5), pages 318-337, December.
    4. deB. Harris, Frederick H. & McInish, Thomas H. & Wood, Robert A., 2002. "Security price adjustment across exchanges: an investigation of common factor components for Dow stocks," Journal of Financial Markets, Elsevier, vol. 5(3), pages 277-308, July.
    5. Jing Chen & David G. McMillan & Mike Buckle, 2018. "Information Transmission across European Equity Markets During Crisis Periods," Manchester School, University of Manchester, vol. 86(6), pages 770-788, December.
    6. Mun, Kyung-Chun, 2007. "Volatility and correlation in international stock markets and the role of exchange rate fluctuations," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(1), pages 25-41, February.
    7. Yifan Chen & Limin Yu & Jianhua Gang, 2021. "Half-day trading and spillovers," Frontiers of Business Research in China, Springer, vol. 15(1), pages 1-22, December.
    8. Tse, Yiuman, 1998. "International transmission of information: evidence from the Euroyen and Eurodollar futures markets," Journal of International Money and Finance, Elsevier, vol. 17(6), pages 909-929, December.
    9. Hernandez, Manuel A. & Ibarra, Raul & Trupkin, Danilo R., "undated". "How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 124979, International Association of Agricultural Economists.
    10. Tse, Yiuman & Booth, G. Geoffrey, 1997. "Information shares in international oil futures markets," International Review of Economics & Finance, Elsevier, vol. 6(1), pages 49-56.
    11. Sheng-Yung Yang, 2007. "Inter-day return and volatility dynamics between Japanese ADRs and their underlying securities," Applied Financial Economics, Taylor & Francis Journals, vol. 17(10), pages 837-853.
    12. Wang, Steven Shuye & Firth, Michael, 2004. "Do bears and bulls swim across oceans? Market information transmission between greater China and the rest of the world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(3), pages 235-254, July.
    13. Tswei, Keshin & Lai, Jing-yi, 2009. "Information contents misjudged: Digressive convergence to equilibrium in cointegrated prices," Review of Financial Economics, Elsevier, vol. 18(4), pages 183-189, October.
    14. Keshin Tswei & Jing‐yi Lai, 2009. "Information contents misjudged: Digressive convergence to equilibrium in cointegrated prices," Review of Financial Economics, John Wiley & Sons, vol. 18(4), pages 183-189, October.
    15. Martinez, Valeria & Tse, Yiuman, 2018. "Intraday price discovery analysis in the foreign exchange market of an emerging economy: Mexico," Research in International Business and Finance, Elsevier, vol. 45(C), pages 271-284.
    16. Tse, Yiuman & Martinez, Valeria, 2007. "Price discovery and informational efficiency of international iShares funds," Global Finance Journal, Elsevier, vol. 18(1), pages 1-15.
    17. Paolo Savona & Aurelio Maccario & Chiara Oldani, 2000. "On Monetary Analysis of Derivatives," Open Economies Review, Springer, vol. 11(1), pages 149-175, August.
    18. Tse, Yiuman, 1999. "Round-the-clock market efficiency and home bias: Evidence from the international Japanese government bonds futures markets," Journal of Banking & Finance, Elsevier, vol. 23(12), pages 1831-1860, December.
    19. Wang, Steven Shuye & Meng Rui, Oliver & Firth, Michael, 2002. "Return and volatility behavior of dually-traded stocks: the case of Hong Kong," Journal of International Money and Finance, Elsevier, vol. 21(2), pages 265-293, April.
    20. Kao, Chung-Wei & Wan, Jer-Yuh, 2009. "Information transmission and market interactions across the Atlantic -- an empirical study on the natural gas market," Energy Economics, Elsevier, vol. 31(1), pages 152-161, January.
    21. Yiuman Tse, 2000. "Further Examination Of Price Discovery On The Nyse And Regional Exchanges," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 23(3), pages 331-351, September.
    22. Tao Chen & Liang Wu & Isabel Kit-Ming Yan, 2013. "On the Use of International Commodity Futures Spread for Forecasting China's Net Imports of Commodities," The World Economy, Wiley Blackwell, vol. 36(7), pages 861-879, July.

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    1. Mustafa Caglayan & Sara Maioli & Simona Mateut, 2011. "Inventories and sales uncertainty," Working Papers 2011003, The University of Sheffield, Department of Economics, revised Feb 2011.
    2. Caglayan, Mustafa & Maioli, Sara & Mateut, Simona, 2012. "Inventories, sales uncertainty, and financial strength," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2512-2521.
    3. Barry Scholnick, 1999. "Interest Rate Asymmetries in Long-Term Loan and Deposit Markets," Journal of Financial Services Research, Springer;Western Finance Association, vol. 16(1), pages 5-26, September.
    4. Simona Mateut, 2011. "Reverse trade credit - the use of prepayments by French firms," Discussion Papers 11/12, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).

  36. Lee, Tae-Hwy, 1994. "Spread and volatility in spot and forward exchange rates," Journal of International Money and Finance, Elsevier, vol. 13(3), pages 375-383, June.

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    1. Wang, Kuan-Min & Lee, Yuan-Ming, 2009. "Market volatility and retail interest rate pass-through," Economic Modelling, Elsevier, vol. 26(6), pages 1270-1282, November.
    2. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    3. Mr. Torbjorn I. Becker & Mr. Amadou N Sy, 2005. "Were Bid-Ask Spreads in the Foreign Exchange Market Excessive During the Asian Crisis?," IMF Working Papers 2005/034, International Monetary Fund.
    4. Lien, Donald & Yang, Li, 2008. "Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 187-198, February.
    5. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    6. Huang, Junbo & Tian, Huiting & Shen, Weibing, 2023. "Characteristics and mechanisms of the U.S. stock market spillover effects on the Chinese A-share market: Evidence from 6 A-share broad-based and 31 sector indices," International Review of Financial Analysis, Elsevier, vol. 87(C).
    7. Nina Karnaukh & Angelo Ranaldo & Paul Söderlind, 2015. "Understanding FX Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 28(11), pages 3073-3108.
    8. Md. Abu HASAN, 2017. "Efficiency and Volatility of the Stock Market in Bangladesh: A Macroeconometric Analysis," Turkish Economic Review, KSP Journals, vol. 4(2), pages 239-249, June.
    9. Jonathan Dark, 2004. "Long term hedging of the Australian All Ordinaries Index using a bivariate error correction FIGARCH model," Monash Econometrics and Business Statistics Working Papers 7/04, Monash University, Department of Econometrics and Business Statistics.
    10. Choudhry, Taufiq, 2009. "Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 58-65, March.
    11. Banti, Chiara & Phylaktis, Kate, 2015. "FX market liquidity, funding constraints and capital flows," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 114-134.
    12. Jahangir Sultan & Mohammad Hasan, 2008. "The effectiveness of dynamic hedging: evidence from selected European stock index futures," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 469-488.
    13. Emenike Kalu O. & Odili Okwuchukwu, 2014. "Stock Market Return Volatility and Macroeconomic Variables in Nigeria," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 2(2), pages 75-82.
    14. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    15. Emeka Nkoro & Aham Kelvin Uko, 2013. "A Generalized Autoregressive Conditional Heteroskedasticity Model of the Impact of Macroeconomic Factors on Stock Returns: Empirical Evidence from the Nigerian Stock Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(4), pages 38-51, October.
    16. Manolis G. Kavussanos & Ilias D. Visvikis & Panayotis D. Alexakis, 2008. "The Lead‐Lag Relationship Between Cash and Stock Index Futures in a New Market," European Financial Management, European Financial Management Association, vol. 14(5), pages 1007-1025, November.
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    18. Ah-Boon Sim & Ralf Zurbruegg, 2001. "Optimal hedge ratios and alternative hedging strategies in the presence of cointegrated time-varying risks," The European Journal of Finance, Taylor & Francis Journals, vol. 7(3), pages 269-283.
    19. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    20. Frankel, Jeffrey A. & Rose, Andrew K., 1995. "A Survey of Empirical Research on Nominal Exchange Rates," Center for International and Development Economics Research (CIDER) Working Papers 233409, University of California-Berkeley, Department of Economics.
    21. Taufiq Choudhry & Hao Wu, 2008. "Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 670-689.
    22. Zhong, Maosen & Darrat, Ali F. & Otero, Rafael, 2004. "Price discovery and volatility spillovers in index futures markets: Some evidence from Mexico," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 3037-3054, December.
    23. Choudhry, Taufiq, 2003. "Short-run deviations and optimal hedge ratio: evidence from stock futures," Journal of Multinational Financial Management, Elsevier, vol. 13(2), pages 171-192, April.
    24. Donald Lien & Chongfeng Wu & Li Yang & Chunyang Zhou, 2013. "Dynamic and Asymmetric Dependences Between Chinese Yuan and Other Asia‐Pacific Currencies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(8), pages 696-723, August.
    25. Wang, Kai-Li & Fawson, Christopher & Chen, Mei-Ling & Wu, An-Chi, 2014. "Characterizing information flows among spot, deliverable forward and non-deliverable forward exchange rate markets: A cross-country comparison," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 115-137.
    26. Becker, Torbjorn & Sy, Amadou, 2006. "Were bid-ask spreads in the FX market excessive during the Asian crisis?," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 434-449.
    27. Md. Abu HASAN & Anita ZAMAN, 2017. "Volatility Nexus Between Stock Market And Macroeconomic Variables In Bangladesh: An Extended Garch Approach," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 64(2), pages 233-243, June.
    28. Kavussanos, Manolis G. & Visvikis, Ilias D., 2004. "Market interactions in returns and volatilities between spot and forward shipping freight markets," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 2015-2049, August.
    29. Kuan-Min Wang, 2010. "Expected and Unexpected Impulses of Monetary Policy on the Interest Pass-Through Mechanism in Asian Countries," Annals of Economics and Finance, Society for AEF, vol. 11(1), pages 95-137, May.
    30. Manolis Kavussanos & Ilias Visvikis, 2008. "Hedging effectiveness of the Athens stock index futures contracts," The European Journal of Finance, Taylor & Francis Journals, vol. 14(3), pages 243-270.
    31. Kao, Wei-Shun & Lin, Chu-Hsiung & Changchien, Chang-Cheng & Wu, Chien-Hui, 2017. "Return distribution, leverage effect and spot-futures spread on the hedging effectiveness," Finance Research Letters, Elsevier, vol. 22(C), pages 158-162.
    32. Walid M. A. Ahmed, 2014. "Dynamic interactions between Egyptian equity and currency markets prior to and during political unrest," Applied Financial Economics, Taylor & Francis Journals, vol. 24(20), pages 1347-1359, October.
    33. Banti, Chiara & Phylaktis, Kate & Sarno, Lucio, 2012. "Global liquidity risk in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 267-291.

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    Cited by:

    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
    2. Zhuo Qiao & Keith Lam, 2011. "Granger causal relations among Greater China stock markets: a nonlinear perspective," Applied Financial Economics, Taylor & Francis Journals, vol. 21(19), pages 1437-1450.
    3. Ng, S., 1995. "Looking for Evidence of Speculative Stockholding in Commodity Markets," Cahiers de recherche 9514, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. William Barnett & Alfredo Medio & Apostolos Serletis, 2012. "Nonlinear And Complex Dynamics In Economics," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201223, University of Kansas, Department of Economics, revised Sep 2012.
    5. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.
    6. Aviral Kumar Tiwari & Adeolu O. Adewuyi & Olabanji B. Awodumi & David Roubaud, 2022. "Relationship between stock returns and inflation: New evidence from the US using wavelet and causality methods," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4515-4540, October.
    7. Rebeca Jiménez-Rodríguez, 2004. "Oil Price Shocks: Testing for Non-linearity," CSEF Working Papers 115, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    8. de Lima, Pedro J. F., 1997. "On the robustness of nonlinearity tests to moment condition failure," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 251-280.
    9. Stan Hurn & Ralf Becker, 2009. "Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity," Economic Analysis and Policy, Elsevier, vol. 39(2), pages 311-326, September.
    10. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    11. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    12. Arne Steinkraus, 2019. "Estimating Treatment Effects With Artificial Neural Nets – A Comparison to Synthetic Control Method," Economics Bulletin, AccessEcon, vol. 39(4), pages 2778-2791.
    13. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
    14. Heather M. Anderson & Farshid Vahid, 2003. "Nonlinear Correlograms and Partial Autocorrelograms," Monash Econometrics and Business Statistics Working Papers 19/03, Monash University, Department of Econometrics and Business Statistics.
    15. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    16. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    17. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    18. Norman R. Swanson & Valentina Corradi & Andres Fernandez, 2011. "Information in the Revision Process of Real-Time Datasets," Departmental Working Papers 201107, Rutgers University, Department of Economics.
    19. Zacharias Psaradakis & Marián Vávra, 2015. "Portmanteau Tests for Linearity of Stationary Time Series," Birkbeck Working Papers in Economics and Finance 1514, Birkbeck, Department of Economics, Mathematics & Statistics.
    20. Kapetanios, G. & Tzavalis, E., 2010. "Modeling structural breaks in economic relationships using large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 417-436, March.
    21. Daniel Santín González & Rosa Romay López, 2003. "El control del gasto público por incapacidad temporal mediante redes neuronales," Hacienda Pública Española / Review of Public Economics, IEF, vol. 165(2), pages 53-78, June.
    22. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La inflación en Colombia: una aproximación desde las redes neuronales," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 20(41-42), pages 143-214, June.
    23. Corradi, Valentina & Swanson, Norman R. & White, Halbert, 2000. "Testing for stationarity-ergodicity and for comovements between nonlinear discrete time Markov processes," Journal of Econometrics, Elsevier, vol. 96(1), pages 39-73, May.
    24. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    25. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    26. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    27. Mototsugu Shintani, 2004. "A Dynamic Factor Approach to Nonlinear Stability Analysis," Vanderbilt University Department of Economics Working Papers 0418, Vanderbilt University Department of Economics.
    28. T Tang, 2009. "Testing for Non-linearity in the Balancing Item of Balance of Payments Accounts: The Case of 20 Industrial Countries," Economic Issues Journal Articles, Economic Issues, vol. 14(2), pages 107-124, September.
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Chapters

  1. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Efficient Combined Estimation under Structural Breaks," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 119-142, Emerald Group Publishing Limited.
    See citations under working paper version above.
  2. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    See citations under working paper version above.
  3. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," Advances in Econometrics, in: 30th Anniversary Edition, pages 171-196, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

Books

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