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Marcelo Fernandes

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.

Working papers

  1. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..

    Cited by:

    1. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    2. Sebastiano Michele Zema & Francesco Cordoni, 2023. "A non-Normal framework for price discovery: The independent component based information shares measure," LEM Papers Series 2023/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    4. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    5. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

  2. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2019. "Smoothing quantile regressions," Papers 1905.08535, arXiv.org, revised Aug 2019.

    Cited by:

    1. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    2. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    3. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    4. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
    5. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    6. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
    7. Mario Forni & Luca Gambetti & Nicolò Maffei-Faccioli & Luca Sala, 2023. "The impact of financial shocks on the forecast distribution of output and inflation," Working Paper 2023/3, Norges Bank.
    8. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Smoothing the Nonsmoothness," Papers 2309.16348, arXiv.org.
    9. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    10. Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Feb 2023.
    11. Kean Ming Tan & Lan Wang & Wen‐Xin Zhou, 2022. "High‐dimensional quantile regression: Convolution smoothing and concave regularization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 205-233, February.
    12. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
    13. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    14. Tae-Hwy Lee & Aman Ullah & He Wang, 2023. "The Second-order Bias and Mean Squared Error of Quantile Regression Estimators," Working Papers 202313, University of California at Riverside, Department of Economics.

  3. Fernandes, Marcelo & Novaes, Walter, 2017. "The government as a large shareholder: impact on corporate governance," Textos para discussão 458, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Augusto Carvalho & Bernardo Guimaraes, 2016. "State-controlled companies and political risk: Evidence from the 2014 Brazilian election," Discussion Papers 1702, Centre for Macroeconomics (CFM).
    2. Carlos Viana de Carvalho & Eduardo Zilberman & Ruy Ribeiro, "undated". "Sentiment, Electoral Uncertainty and Stock Returns," Textos para discussão 655, Department of Economics PUC-Rio (Brazil).

  4. Scherrer, Cristina Mabel & Fernandes, Marcelo, 2017. "Disentangling the effect of private and public cash flows on firm value," Textos para discussão 443, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Fernandes, Marcelo & Scherrer, Cristina Mabel, 2013. "Price discovery in dual-class shares across multiple markets," Textos para discussão 344, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

  5. Fausto Vieira & Fernando Chague & Marcelo Fernandes, 2016. "Forecasting the Brazilian Yield Curve Using Forward-Looking Variables," Working Papers 799, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
    2. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    3. Firdous Ahmad Shah & Lokenath Debnath, 2017. "Wavelet Neural Network Model for Yield Spread Forecasting," Mathematics, MDPI, vol. 5(4), pages 1-15, November.
    4. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Hang Thi Thu Vu, 2021. "The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries," JRFM, MDPI, vol. 14(2), pages 1-23, February.
    5. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  6. Marcelo Fernandes & Ms. Deniz O Igan & Marcelo Pinheiro, 2015. "March Madness in Wall Street: (What) Does the Market Learn from Stress Tests?," IMF Working Papers 2015/271, International Monetary Fund.

    Cited by:

    1. Ongena, Steven & Kok, Christoffer & Müller, Carola & Pancaro, Cosimo, 2021. "The disciplining effect of supervisory scrutiny in the EU-wide stress test," CEPR Discussion Papers 16157, C.E.P.R. Discussion Papers.
    2. Beverly Hirtle & Anna Kovner, 2020. "Bank Supervision," Staff Reports 952, Federal Reserve Bank of New York.
    3. Thomas Ian Schneider & Philip E. Strahan & Jun Yang, 2020. "Bank Stress Testing: Public Interest or Regulatory Capture?," NBER Working Papers 26887, National Bureau of Economic Research, Inc.
    4. Lukas Ahnert & Pascal Vogt & Volker Vonhoff & Florian Weigert, 2020. "Regulatory stress testing and bank performance," European Financial Management, European Financial Management Association, vol. 26(5), pages 1449-1488, November.
    5. Parlatore Siritto, Cecilia & Philippon, Thomas, 2022. "Designing Stress Scenarios," CEPR Discussion Papers 17145, C.E.P.R. Discussion Papers.
    6. Beutel, Johannes & Metiu, Norbert & Stockerl, Valentin, 2021. "Toothless tiger with claws? Financial stability communication, expectations, and risk-taking," Discussion Papers 05/2021, Deutsche Bundesbank.
    7. Philippon, Thomas & Camara, Boubacar & Pessarossi, Pierre, 2017. "Backtesting European Stress Tests," CEPR Discussion Papers 11805, C.E.P.R. Discussion Papers.
    8. Iorgova, Silvia & Ross, Chase P., 2023. "Investor information and bank instability during the European debt crisis," Journal of Financial Stability, Elsevier, vol. 64(C).
    9. Paul Glasserman & Mike Li, 2022. "Should Bank Stress Tests Be Fair?," Papers 2207.13319, arXiv.org, revised May 2023.
    10. García, Raffi E. & Steele, Suzanne, 2022. "Stress testing and bank business patterns: A regression discontinuity study," Journal of Banking & Finance, Elsevier, vol. 135(C).
    11. Luu, Hiep Ngoc & Vo, Xuan Vinh, 2021. "The Impact of Supervisory Stress Tests on Bank Ex-Ante Risk-Taking Behaviour: Empirical Evidence from a Quasi-Natural Experiment," International Review of Financial Analysis, Elsevier, vol. 75(C).
    12. Abad, Pilar & Robles, M.-Dolores & Alonso Orts, Carlos, 2023. "Stress testing programs and credit risk opacity of banks: USA vs Europe," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    13. Mr. Luis Brandão-Marques, 2016. "Stock Market Liquidity in Chile," IMF Working Papers 2016/223, International Monetary Fund.
    14. Lucas Hafemann & Peter Tillmann, 2021. "Lending Standards and the Business Cycle: Evidence from Loan Survey Releases," MAGKS Papers on Economics 202131, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Flannery, Mark & Hirtle, Beverly & Kovner, Anna, 2017. "Evaluating the information in the federal reserve stress tests," Journal of Financial Intermediation, Elsevier, vol. 29(C), pages 1-18.
    16. Christos Floros & Efstathios Karpouzis & Nikolaos Daskalakis, 2024. "Stock Markets and Stress Test Announcements: Evidence from European Banks," Economies, MDPI, vol. 12(7), pages 1-11, July.
    17. Andrew F. Haughwout & Donald P. Morgan & Michael Neubauer & Maxim L. Pinkovskiy & Wilbert Van der Klaauw, 2022. "Nonconforming Preferences: Jumbo Mortgage Lending and Large Bank Stress Tests," Staff Reports 1029, Federal Reserve Bank of New York.
    18. Lazzari, Valter & Vena, Luigi & Venegoni, Andrea, 2017. "Stress tests and asset quality reviews of banks: A policy announcement tool," Journal of Financial Stability, Elsevier, vol. 32(C), pages 86-98.
    19. Ferretti, Riccardo & Venturelli, Valeria & Azzaretto, Alessandro, 2023. "Does individual SREP results reveal real news?," Finance Research Letters, Elsevier, vol. 57(C).
    20. Nguyen, Thach Vu Hong & Ahmed, Shamim & Chevapatrakul, Thanaset & Onali, Enrico, 2020. "Do stress tests affect bank liquidity creation?," Journal of Corporate Finance, Elsevier, vol. 64(C).
    21. Kristle Romero Cortes & Yuliya Demyanyk & Lei Li & Elena Loutskina & Philip E. Strahan, 2018. "Stress Tests and Small Business Lending," Working Papers (Old Series) 1802, Federal Reserve Bank of Cleveland.
    22. Luca Guerrieri & Michele Modugno, 2021. "The Information Content of Stress Test Announcements," Finance and Economics Discussion Series 2021-012, Board of Governors of the Federal Reserve System (U.S.).
    23. Durrani, Agha & Ongena, Steven & Ponte Marques, Aurea, 2022. "The certification role of the EU-wide stress testing exercises in the stock market. What can we learn from the stress tests (2014-2021)?," Working Paper Series 2711, European Central Bank.
    24. Cornett, Marcia Millon & Minnick, Kristina & Schorno, Patrick J. & Tehranian, Hassan, 2020. "An examination of bank behavior around Federal Reserve stress tests," Journal of Financial Intermediation, Elsevier, vol. 41(C).

  7. Marcelo Fernandes & Cristina M. Scherrer, 2014. "Price discovery in dual-class shares across multiple markets," CREATES Research Papers 2014-10, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Gustavo Fruet Dias & Marcelo Fernandes & Cristina Mabel Scherrer, 2019. "Price discovery in a continuous-time setting," University of East Anglia School of Economics Working Paper Series 2019-02, School of Economics, University of East Anglia, Norwich, UK..
    2. David Evangelista & Yuri Saporito & Yuri Thamsten, 2022. "Price formation in financial markets: a game-theoretic perspective," Papers 2202.11416, arXiv.org.
    3. Lien, Donald & Shrestha, Keshab & Lee, Lianne Mei Quin, 2022. "Analytical properties of Hasbrouck and generalized information shares," Finance Research Letters, Elsevier, vol. 49(C).
    4. Santos, Francisco Luna & Garcia, Márcio Gomes Pinto & Medeiros, Marcelo Cunha, 2015. "Price Discovery in Brazilian FX Markets," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    5. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    6. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    7. Scherrer, Cristina Mabel, 2021. "Information processing on equity prices and exchange rate for cross-listed stocks," Journal of Financial Markets, Elsevier, vol. 54(C).
    8. Gustavo Fruet Dias & Marcelo Fernandes & Cristina M. Scherrer, 2016. "Component shares in continuous time," CREATES Research Papers 2016-25, Department of Economics and Business Economics, Aarhus University.
    9. Osama Ahmed, 2021. "Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach," Mathematics, MDPI, vol. 9(2), pages 1-21, January.
    10. Alexander, Carol & Heck, Daniel F., 2020. "Price discovery in Bitcoin: The impact of unregulated markets," Journal of Financial Stability, Elsevier, vol. 50(C).
    11. Dias, Gustavo Fruet & Fernandes, Marcelo & Scherrer, Cristina Mabel, 2017. "Improving on daily measures of price discovery," Textos para discussão 444, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    12. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    13. Ahmed, Osama, 2021. "Assessing the current situation of the world wheat market leadership: Using the semi-parametric approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(2).

  8. Fernandes, Marcelo & Preumont, Pierre-Yves, 2014. "The finite-sample size of the BDS test for GARCH standardized residuals," Textos para discussão 361, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Xin Huang & Han Lin Shang & David Pitt, 2022. "A model sufficiency test using permutation entropy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 1017-1036, August.
    2. Luo, Wenya & Bai, Zhidong & Zheng, Shurong & Hui, Yongchang, 2020. "A modified BDS test," Statistics & Probability Letters, Elsevier, vol. 164(C).

  9. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2013. "Modeling and predicting the CBOE market volatility index," Textos para discussão 342, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Falik Shear & Badar Nadeem Ashraf & Mohsin Sadaqat, 2020. "Are Investors’ Attention and Uncertainty Aversion the Risk Factors for Stock Markets? International Evidence from the COVID-19 Crisis," Risks, MDPI, vol. 9(1), pages 1-15, December.
    2. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    3. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "How are VIX and Stock Index ETF Related?," Documentos de Trabajo del ICAE 2016-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. Liu, Qing & Wang, Shouyang & Sui, Cong, 2023. "Risk appetite and option prices: Evidence from the Chinese SSE50 options market," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    6. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    7. Balcilar, Mehmet & Hammoudeh, Shawkat & Toparli, Elif Akay, 2018. "On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach," Energy Economics, Elsevier, vol. 74(C), pages 813-827.
    8. Choe, Geon Ho & Choi, So Eun & Jang, Hyun Jin, 2020. "Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    10. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    11. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    12. Barletta, Andrea & Santucci de Magistris, Paolo & Violante, Francesco, 2019. "A non-structural investigation of VIX risk neutral density," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 1-20.
    13. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    14. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    15. Gloria Gonzalez-Rivera & Joao Henrique Mazzeu & Esther Ruiz & Helena Veiga, 2017. "A Bootstrap Approach for Generalized Autocontour Testing. Implications for VIX Forecast Densities," Working Papers 201709, University of California at Riverside, Department of Economics.
    16. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    17. Guglielmo Maria Caporale & Luis A. Gil-Alana & Tommaso Trani, 2018. "Brexit and Uncertainty in Financial Markets," CESifo Working Paper Series 6874, CESifo.
    18. Liu, Qiang & Guo, Shuxin & Qiao, Gaoxiu, 2015. "VIX forecasting and variance risk premium: A new GARCH approach," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 314-322.
    19. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    20. Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
    21. Chune Young Chung & Doojin Ryu & Kainan Wang & Blerina Bela Zykaj, 2018. "Optionable Stocks and Mutual Fund Performance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 390-412, March.
    22. VDMV Lakshmi & Garima Sisodia & Anto Joseph & Aviral Kumar Tiwari, 2024. "The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities during pre‐COVID and COVID periods," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3007-3022, July.
    23. Ballestra, Luca Vincenzo & Guizzardi, Andrea & Palladini, Fabio, 2019. "Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1250-1262.
    24. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    25. Arindam Banerjee, 2019. "Forecasting of India VIX as a Measure of Sentiment," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 268-276.
    26. Chen, Bin-xia & Sun, Yan-lin, 2022. "The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    27. Pierre J. Venter & Eben Maré, 2020. "GARCH Generated Volatility Indices of Bitcoin and CRIX," JRFM, MDPI, vol. 13(6), pages 1-15, June.
    28. Troster, Victor & Bouri, Elie & Roubaud, David, 2019. "A quantile regression analysis of flights-to-safety with implied volatilities," Resources Policy, Elsevier, vol. 62(C), pages 482-495.
    29. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    30. Wang, Zhenkun & Bouri, Elie & Ferreira, Paulo & Shahzad, Syed Jawad Hussain & Ferrer, Román, 2022. "A grey-based correlation with multi-scale analysis: S&P 500 VIX and individual VIXs of large US company stocks," Finance Research Letters, Elsevier, vol. 48(C).
    31. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
    32. Dicle, Mehmet F. & Levendis, John, 2020. "Historic risk and implied volatility," Global Finance Journal, Elsevier, vol. 45(C).
    33. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    34. Jin, Jiayu & Han, Liyan & Xu, Yang, 2022. "Does the SDR stabilize investing in commodities?," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 160-172.
    35. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    36. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, Department of Economics and Business Economics, Aarhus University.
    37. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    38. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    39. Chang, C-L. & Hsieh, T-L. & McAleer, M.J., 2018. "Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK," Econometric Institute Research Papers EI2018-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    40. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
    41. Akhilesh Prasad & Priti Bakhshi, 2022. "Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning," JRFM, MDPI, vol. 15(12), pages 1-26, November.
    42. David E Allen & Vince Hooper, 2018. "Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non-Linear Models," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    43. González-Rivera, Gloria & Veiga, Helena, 2016. "A Bootstrap Approach for Generalized Autocontour Testing," DES - Working Papers. Statistics and Econometrics. WS 23457, Universidad Carlos III de Madrid. Departamento de Estadística.
    44. Peterburgsky, Stanley, 2024. "Size, value and volatility," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 752-763.
    45. Fei, Tianlun & Liu, Xiaoquan, 2021. "Herding and market volatility," International Review of Financial Analysis, Elsevier, vol. 78(C).
    46. Taylor, Nick, 2019. "Forecasting returns in the VIX futures market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1193-1210.
    47. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    48. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    49. Delis, Panagiotis & Degiannakis, Stavros & Giannopoulos, Kostantinos, 2021. "What should be taken into consideration when forecasting oil implied volatility index?," MPRA Paper 110831, University Library of Munich, Germany.
    50. Junting Liu & Qi Wang & Yuanyuan Zhang, 2024. "VIX option pricing through nonaffine GARCH dynamics and semianalytical formula," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1189-1223, July.
    51. Hoerova, Marie & Bekaert, Geert, 2014. "The VIX, the variance premium and stock market volatility," Working Paper Series 1675, European Central Bank.
    52. Song, Wonho & Ryu, Doojin & Webb, Robert I., 2016. "Overseas market shocks and VKOSPI dynamics: A Markov-switching approach," Finance Research Letters, Elsevier, vol. 16(C), pages 275-282.
    53. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    54. Xiao, Jihong & Wen, Fenghua & Zhao, Yupei & Wang, Xiong, 2021. "The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 311-333.
    55. Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Documentos de Trabajo del ICAE 2011-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    56. 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).
    57. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    58. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, vol. 37(C), pages 242-251.
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    61. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    62. Xiao, Jihong & Wang, Yudong, 2022. "Good oil volatility, bad oil volatility, and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 953-966.
    63. Qadan, Mahmoud & Jacob, Maram, 2022. "The value premium and investors' appetite for risk," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 194-219.
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    65. Anupam Dutta & Debojyoti Das, 2022. "Forecasting realized volatility: New evidence from time‐varying jumps in VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2165-2189, December.
    66. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
    67. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
    68. Christopher Krauss & Xuan Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01515120, HAL.
    69. 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.
    70. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Tripathy, Trilochan, 2020. "Volatility persistence in the Russian stock market," Finance Research Letters, Elsevier, vol. 32(C).
    71. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stober, 2016. "Regime switching vine copula models for global equity and volatility indices," Papers 1604.05598, arXiv.org.
    72. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    73. Geng, Jiang-Bo & Chen, Fu-Rui & Ji, Qiang & Liu, Bing-Yue, 2021. "Network connectedness between natural gas markets, uncertainty and stock markets," Energy Economics, Elsevier, vol. 95(C).
    74. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    75. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    76. Ioannis Dokas & Georgios Oikonomou & Minas Panagiotidis & Eleftherios Spyromitros, 2023. "Macroeconomic and Uncertainty Shocks’ Effects on Energy Prices: A Comprehensive Literature Review," Energies, MDPI, vol. 16(3), pages 1-35, February.
    77. Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
    78. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.
    79. Jeng-Bau Lin & Wei Tsai, 2019. "The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment," Energies, MDPI, vol. 12(15), pages 1-17, August.
    80. Guglielmo Maria Caporale & Luis A. Gil-Alana & Trilochan Tripathy, 2018. "Persistence in the Russian Stock Market Volatility Indices," CESifo Working Paper Series 7243, CESifo.
    81. Tuncer Yılmaz & Bülent Yıldız, 2022. "Yatırımcıların Risk İştahı Endeksi İle Korku Endeksleri Arasındaki İlişki: Türkiye’de ARDL İle Ampirik Bir Uygulama," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(3), pages 646-676.
    82. Reiter-Gavish, Liron & Qadan, Mahmoud & Yagil, Joseph, 2021. "Financial advice: Who Exactly Follows It?," Research in Economics, Elsevier, vol. 75(3), pages 244-258.
    83. Chen & Jo-Hui & Hussain & Sabbor & Chen & Fu-Ying, 2023. "The Relationship between VIX and Technical Indicator: The Analysis of Shared-Frailty Model," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(3), pages 1-5.
    84. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    85. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    86. 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.
    87. Saffet Akdag & Ömer İskenderoglu & Andrew Adewale Alola, 2020. "The volatility spillover effects among risk appetite indexes: insight from the VIX and the rise," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 49-65, April.
    88. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Effects of the US stock market return and volatility on the VKOSPI," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-34.
    89. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Modeling and predicting the market volatility index: The case of VKOSPI," Economics Discussion Papers 2015-7, Kiel Institute for the World Economy (IfW Kiel).
    90. Lei, Heng & Xue, Minggao & Liu, Huiling & Ye, Jing, 2023. "Precious metal as a safe haven for global ESG stocks: Portfolio implications for socially responsible investing," Resources Policy, Elsevier, vol. 80(C).
    91. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    92. Bruno Deschamps & Tianlun Fei & Ying Jiang & Xiaoquan Liu, 2022. "Procyclical volatility in Chinese stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1117-1144, April.
    93. 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).
    94. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.
    95. Ji, Qiang & Liu, Bing-Yue & Nehler, Henrik & Uddin, Gazi Salah, 2018. "Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach," Energy Economics, Elsevier, vol. 76(C), pages 115-126.
    96. Bahram Adrangi & Arjun Chatrath & Joseph Macri & Kambiz Raffiee, 2019. "Dynamic Responses of Major Equity Markets to the US Fear Index," JRFM, MDPI, vol. 12(4), pages 1-23, September.
    97. Pham, Linh & Do, Hung Xuan, 2022. "Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management," Energy Economics, Elsevier, vol. 112(C).
    98. Jung Park, Yuen & Kutan, Ali M. & Ryu, Doojin, 2019. "The impacts of overseas market shocks on the CDS-option basis," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 622-636.
    99. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).
    100. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    101. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.
    102. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    103. Ouandlous, Arav & Barkoulas, John T. & Alhaj-Yaseen, Yaseen, 2018. "Persistence and discontinuity in the VIX dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 333-344.
    104. Sharma, Aarzoo, 2022. "A comparative analysis of the financialization of commodities during COVID-19 and the global financial crisis using a quantile regression approach," Resources Policy, Elsevier, vol. 78(C).
    105. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stöber, 2017. "Regime Switching Vine Copula Models for Global Equity and Volatility Indices," Econometrics, MDPI, vol. 5(1), pages 1-38, January.
    106. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    107. Uddin, Moshfique & Chowdhury, Anup & Anderson, Keith & Chaudhuri, Kausik, 2021. "The effect of COVID – 19 pandemic on global stock market volatility: Can economic strength help to manage the uncertainty?," Journal of Business Research, Elsevier, vol. 128(C), pages 31-44.
    108. Qadan, Mahmoud & Aharon, David Y., 2019. "How much happiness can we find in the U.S. fear Index?," Finance Research Letters, Elsevier, vol. 30(C), pages 246-258.
    109. Philip Stahl, 2022. "Asymptotic extrapolation of model-free implied variance: exploring structural underestimation in the VIX Index," Review of Derivatives Research, Springer, vol. 25(3), pages 315-339, October.
    110. Iuri H. Ferreira & Marcelo C. Medeiros, 2021. "Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach," Papers 2112.15108, arXiv.org.
    111. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    112. Smales, L.A., 2016. "Risk-on/Risk-off: Financial market response to investor fear," Finance Research Letters, Elsevier, vol. 17(C), pages 125-134.
    113. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    114. Curi, Claudia & Murgia, Lucia Milena, 2023. "Forecast Targeting and Financial Stability: Evidence from the European Central Bank and Bank of England," Finance Research Letters, Elsevier, vol. 51(C).

  10. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2013. "Conditional alphas and realized betas," Textos para discussão 341, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Tim Bollerslev & Sophia Zhengzi Li & Viktor Todorov, 2014. "Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns," CREATES Research Papers 2014-48, Department of Economics and Business Economics, Aarhus University.
    2. Bollerslev, Tim & Li, Sophia Zhengzi & Todorov, Viktor, 2016. "Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 120(3), pages 464-490.

  11. Fernandes, Marcelo & Mergulhão, João de Mendonça, 2013. "Anticipatory effects in the FTSE 100 index revisions," Textos para discussão 345, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Afego, Pyemo N., 2017. "Effects of changes in stock index compositions: A literature survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 228-239.
    2. Tseng, Yun-lan & Pan, Ging-ginq, 2024. "Do anticipated changes in the MSCI Taiwan index drive investor behavior?," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 563-580.
    3. Friedrich-Carl Franz, 2020. "Forecasting index changes in the German DAX family," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 135-153, March.
    4. Gang Chu & John W. Goodell & Xiao Li & Yongjie Zhang, 2023. "Understanding short‐term price pressure from index reconstitutions: Evidence from the CSI 300," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(2), pages 2421-2440, June.
    5. Chen, Hung-Ling & Shiu, Cheng-Yi & Wei, Hui-Shan, 2019. "Price effect and investor awareness: Evidence from MSCI Standard Index reconstitutions," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 93-112.

  12. Fernandes, Marcelo & Medeiros, Marcelo C. & Veiga, Alvaro, 2013. "A (semi-)parametric functional coefficient autoregressive conditional duration model," Textos para discussão 343, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    Cited by:

    1. Chor-Yiu Sin, 2014. "Qmle Of A Standard Exponential Acd Model: Asymptotic Distribution And Residual Correlation," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-10.
    2. Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1291-1320, October.
    3. Pooi AH-HIN & Ng KOK-HAUR & Soo HUEI-CHING, 2016. "Modelling and Forecasting with Financial Duration Data Using Non-linear Model," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 79-92.

  13. Marcelo FERNANDES & Eduardo F. MENDES & Olivier SCAILLET, 2011. "Testing for Symmetry and Conditional Symmetry Using Asymmetric Kernels," Swiss Finance Institute Research Paper Series 11-32, Swiss Finance Institute.

    Cited by:

    1. Abadir, Karim M. & Lawford, Steve, 2004. "Optimal asymmetric kernels," Economics Letters, Elsevier, vol. 83(1), pages 61-68, April.
    2. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    3. Masayuki Hirukawa & Mari Sakudo, 2016. "Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels," Econometrics, MDPI, vol. 4(2), pages 1-27, June.
    4. Funke, Benedikt & Hirukawa, Masayuki, 2019. "Nonparametric estimation and testing on discontinuity of positive supported densities: a kernel truncation approach," Econometrics and Statistics, Elsevier, vol. 9(C), pages 156-170.

  14. Danilo Coelho & Marcelo Fernandes e Miguel N. Foguel, 2009. "Capital Estrangeiro e Diferenciais de Gênero nas Promoções: Evidências da Indústria de Transformação Brasileira," Discussion Papers 1447, Instituto de Pesquisa Econômica Aplicada - IPEA.

    Cited by:

    1. Eduardo P. S. Fiuza & Barbara Caballero, 2015. "Estimations od Generic Drug Entry in Brazil using count versus ordered models," Discussion Papers 0186, Instituto de Pesquisa Econômica Aplicada - IPEA.

  15. Araújo, Fabio & Issler, João Victor & Fernandes, Marcelo, 2006. "A stochastic discount factor approach to asset pricing using panel data," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 628, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Lima, Luiz Renato & Néri, Breno Pinheiro, 2007. "Comparing Value-at-Risk Methodologies," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(1), May.
    2. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2007. "A panel data approach to economic forecasting: the bias-corrected average forecast," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 650, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Xiao, Zhijie & Lima, Luiz Renato Regis de Oliveira, 2006. "Testing covariance stationarity," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 632, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    4. Lima, Luiz Renato Regis de Oliveira & Sampaio, Raquel Menezes Bezerra & Gaglianone, Wagner Piazza, 2006. "Debt ceiling and fiscal sustainability in Brazil: a quantile autoregression approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 631, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

  16. Fernandes, Marcelo & Rocha, Marco Aurélio dos Santos, 2006. "Are price limits on futures markets that cool?: evidence from the Brazilian Mercantile and Futures Exchange," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 630, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Zhang, Xiaotao & Zhao, Yuepeng & Wang, Ziqiao, 2024. "Do loosened trading rules restore the stock index futures price discovery ability in China?," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 389-397.
    2. Wong, Woon K. & Chang, Matthew C. & Tu, Anthony H., 2009. "Are magnet effects caused by uninformed traders? Evidence from Taiwan Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 17(1), pages 28-40, January.
    3. Imtiaz Mohammad Sifat & Azhar Mohamad, 2019. "Circuit breakers as market stability levers: A survey of research, praxis, and challenges," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 1130-1169, July.
    4. Wong, Woon K. & Liu, Bo & Zeng, Yong, 2009. "Can price limits help when the price is falling? Evidence from transactions data on the Shanghai Stock Exchange," China Economic Review, Elsevier, vol. 20(1), pages 91-102, March.
    5. Levy, Tamir & Qadan, Mahmod & Yagil, Joseph, 2013. "Predicting the limit-hit frequency in futures contracts," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 141-148.

  17. Araújo, Fabio & Issler, João Victor & Fernandes, Marcelo, 2005. "Estimating the stochastic discount factor without a utility function," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 583, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Athanasopoulos, George & Guillen, Osmani Teixeira Carvalho & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 713, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Bonomo, Marco Antônio Cesar & Terra, Maria Cristina T., 2005. "Special interests and political business cycles," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 597, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Andrei G. Simonassi, 2006. "Estimando A Taxa De Retorno Livre De Risco No Brasil," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 180, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Barbosa, Fernando de Holanda, 2005. "The contagion effect of public debt on monetary policy: the brazilian experience," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 591, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    5. Araújo, Fabio & Issler, João Victor & Fernandes, Marcelo, 2005. "Estimating the stochastic discount factor without a utility function," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 583, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    6. Carlos Enrique Carrasco Gutierrez & Wagner Piazza Gaglianone, 2008. "Evaluating Asset Pricing Models in a Fama-French Framework," Working Papers Series 175, Central Bank of Brazil, Research Department.
    7. Gomes, Fábio Augusto Reis & Issler, João Victor, 2009. "Testing the optimality of aggregate consumption decisions: is there rule-of-thumb behavior?," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 682, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

  18. Fernandes, Marcelo, 2003. "Bounds for the probability distribution function of the linear ACD process," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 488, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

  19. Fernandes, Marcelo & Mota, Bernardo de Sá, 2002. "Desempenho de estimadores de volatilidade na Bolsa de Valores de São Paulo," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 458, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).

  20. Fernandes, Marcelo & Toro, Juan, 2002. "O mecanismo monetário de transmissão na economia brasileira pós-Plano Real," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 443, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Wilson Corrêa & Sidney Caetano, 2013. "Monetary policy and transmission mechanism in Brazil: an empirical model," Empirical Economics, Springer, vol. 45(1), pages 115-135, August.

  21. Fernandes, Marcelo, 2001. "Nonparametric entropy-based tests of independence between stochastic processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 413, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Paulo Ferreira & Andreia Dion'isio & S. M. S. Movahed, 2015. "Assessment of 48 Stock markets using adaptive multifractal approach," Papers 1502.05603, arXiv.org, revised Jul 2017.
    2. George Kapetanios, 2007. "A Test for Serial Dependence Using Neural Networks," Working Papers 609, Queen Mary University of London, School of Economics and Finance.
    3. Paulo Jorge Silveira Ferreira, 2012. "Testing serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece," CEFAGE-UE Working Papers 2012_24, University of Evora, CEFAGE-UE (Portugal).
    4. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    5. Atanu Biswas & Maria Carmen Pardo & Apratim Guha, 2014. "Auto-association measures for stationary time series of categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 487-514, September.
    6. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2014. "Testing Serial Independence via Density-Based Measures of Divergence," Methodology and Computing in Applied Probability, Springer, vol. 16(3), pages 627-641, September.

  22. FERNANDES, Marcelo & GRAMMIG, Joachim, 2001. "A family of autoregressive conditional duration models," LIDAM Discussion Papers CORE 2001036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Giuseppe Cavaliere & Thomas Mikosch & Anders Rahbek & Frederik Vilandt, 2022. "The Econometrics of Financial Duration Modeling," Papers 2208.02098, arXiv.org, revised Dec 2022.
    2. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit.
    3. Lütkepohl, Helmut & Proietti, Tommaso, 2011. "Does the Box-Cox transformation help in forecasting macroeconomic time series?," Working Papers 08/2011, University of Sydney Business School, Discipline of Business Analytics.
    4. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    5. BAUWENS, Luc & HAUTSCH, Nikolaus, 2009. "Modelling financial high frequency data using point processes," LIDAM Reprints CORE 2123, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Taoufik Bouezmarni & Jeroen V.K. Rombouts, 2006. "Nonparametric Density Estimation for Positive Time Series," Cahiers de recherche 06-09, HEC Montréal, Institut d'économie appliquée.
    7. Saulo, Helton & Balakrishnan, Narayanaswamy & Vila, Roberto, 2023. "On a quantile autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 425-448.
    8. Tse, Yiu-Kuen & Dong, Yingjie, 2014. "Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 352-361.
    9. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," LIDAM Discussion Papers CORE 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.
    11. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    12. Wu, Zhengxiao, 2012. "On the intraday periodicity duration adjustment of high-frequency data," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 282-291.
    13. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
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    21. Hujer, Reinhard & Vuletic, Sandra, 2007. "Econometric analysis of financial trade processes by discrete mixture duration models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 635-667, February.
    22. Pipat Wongsaart & Jiti Gao, 2011. "Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 18/11, Monash University, Department of Econometrics and Business Statistics.
    23. Simon Clinet & Yoann Potiron, 2016. "Statistical inference for the doubly stochastic self-exciting process," Papers 1607.05831, arXiv.org, revised Jun 2017.
    24. Zikes, Filip & Barunik, Jozef & Shenai, Nikhil, 2015. "Modeling and forecasting persistent financial durations," FinMaP-Working Papers 36, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    25. Eichler, M. & Grothe, O. & Manner, H. & Türk, D.D.T., 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    26. Wing Lon NG, 2004. "Duration and Order Type Clusters," Econometric Society 2004 Australasian Meetings 272, Econometric Society.
    27. Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1291-1320, October.
    28. COSMA, Antonio & GALLI, Fausto, 2006. "A nonparametric ACD model," LIDAM Discussion Papers CORE 2006067, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    29. Xiaodong Jin & Janusz Kawczak, 2003. "Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 103-124, May.
    30. Danúbia R. Cunha & Roberto Vila & Helton Saulo & Rodrigo N. Fernandez, 2020. "A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data," JRFM, MDPI, vol. 13(3), pages 1-20, March.
    31. Hautsch, Nikolaus, 2008. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3978-4015, December.
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    33. Daniel PREVE & Anders ERIKSSON & Jun YU, 2009. "Forecasting Realized Volatility Using A Nonnegative Semiparametric Model," Working Papers 22-2009, Singapore Management University, School of Economics.
    34. Johannes Bleher & Michael Bleher, 2024. "An Algebraic Framework for the Modeling of Limit Order Books," Papers 2406.04969, arXiv.org.
    35. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    36. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
    37. N. Balakrishna & Hira L. Koul, 2017. "Varying kernel marginal density estimator for a positive time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 531-552, July.
    38. Kulan Ranasinghe & Mervyn J. Silvapulle, 2008. "Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown," Monash Econometrics and Business Statistics Working Papers 5/08, Monash University, Department of Econometrics and Business Statistics.
    39. Wei Sun & Svetlozar Rachev & Frank Fabozzi & Petko Kalev, 2008. "Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration," Annals of Finance, Springer, vol. 4(2), pages 217-241, March.
    40. Cho, Jin Seo & White, Halbert, 2010. "Testing for unobserved heterogeneity in exponential and Weibull duration models," Journal of Econometrics, Elsevier, vol. 157(2), pages 458-480, August.
    41. Laurini, Márcio Poletti & Furlani, Luiz Gustavo Cassilatti & Portugal, Marcelo Savino, 2008. "Empirical market microstructure: An analysis of the BRL/US$ exchange rate market," Emerging Markets Review, Elsevier, vol. 9(4), pages 247-265, December.
    42. Fernandes, Marcelo, 2003. "Bounds for the probability distribution function of the linear ACD process," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 488, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    43. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.
    44. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.
    45. Dungey, Mardi & Henry, Olan & McKenzie, Michael, 2010. "From Trade-to-Trade in US Treasuries," Working Papers 10446, University of Tasmania, Tasmanian School of Business and Economics, revised 01 May 2010.
    46. Luintel, Kul B & Xu, Yongdeng, 2013. "Testing weak exogeneity in multiplicative error models," Cardiff Economics Working Papers E2013/6, Cardiff University, Cardiff Business School, Economics Section.
    47. Pyrlik, Vladimir, 2013. "Autoregressive conditional duration as a model for financial market crashes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6041-6051.
    48. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    49. Marcelo Fernandes & Marcelo C. Medeiros & Alvaro Veiga, 2016. "A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1221-1250, August.
    50. Johannes Bleher & Michael Bleher & Thomas Dimpfl, 2020. "From orders to prices: A stochastic description of the limit order book to forecast intraday returns," Papers 2004.11953, arXiv.org, revised May 2021.
    51. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
    52. Zhang Zongxin & Zhang Xiao, 2011. "Trading duration, mutual funds behavior and stock market shock," China Finance Review International, Emerald Group Publishing Limited, vol. 1(3), pages 220-240, July.
    53. Pérez-Rodríguez, Jorge V. & Gómez-Déniz, Emilio & Sosvilla-Rivero, Simón, 2021. "Testing unobserved market heterogeneity in financial markets: The case of Banco Popular," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 151-160.
    54. Pooi AH-HIN & Ng KOK-HAUR & Soo HUEI-CHING, 2016. "Modelling and Forecasting with Financial Duration Data Using Non-linear Model," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 79-92.
    55. Giovanni De Luca & Giampiero M. Gallo & Danilo Carità, 2017. "Evaluating Combined Forecasts for Realized Volatility Using Asymmetric Loss Functions," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(2), pages 99-111, December.
    56. Fernandes, Marcelo & Medeiros, Marcelo C. & Veiga, Alvaro, 2013. "A (semi-)parametric functional coefficient autoregressive conditional duration model," Textos para discussão 343, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    57. Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.
    58. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    59. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
    60. Monteiro, André A., 2009. "The econometrics of randomly spaced financial data: a survey," DES - Working Papers. Statistics and Econometrics. WS ws097924, Universidad Carlos III de Madrid. Departamento de Estadística.
    61. Magdalena Osinska & Andrzej Dobrzynski & Yochanan Shachmurove, 2016. "Performance Of American And Russian Joint Stock Companies On Financial Market. A Microstructure Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 819-851, December.
    62. Adam Clements & Joanne Fuller & Stan Hurn, 2013. "Semi-parametric Forecasting of Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 89(287), pages 508-521, December.
    63. Giampaoli, Iacopo & Ng, Wing Lon & Constantinou, Nick, 2009. "Analysis of ultra-high-frequency financial data using advanced Fourier transforms," Finance Research Letters, Elsevier, vol. 6(1), pages 47-53, March.
    64. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    65. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
    66. Patrick W. Saart & Jiti Gao & David E. Allen, 2015. "Semiparametric Autoregressive Conditional Duration Model: Theory and Practice," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 849-881, December.
    67. Cavaliere, Giuseppe & Mikosch, Thomas & Rahbek, Anders & Vilandt, Frederik, 2024. "Tail behavior of ACD models and consequences for likelihood-based estimation," Journal of Econometrics, Elsevier, vol. 238(2).
    68. Siakoulis, Vasilios, 2015. "Modeling bank default intensity in the USA using autoregressive duration models," MPRA Paper 64526, University Library of Munich, Germany.
    69. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    70. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    71. Wing Lon NG, 2004. "Duration and Order Type Clusters," Econometric Society 2004 Far Eastern Meetings 730, Econometric Society.
    72. Thomas Dimpfl & Stefania Odelli, 2020. "Bitcoin Price Risk—A Durations Perspective," JRFM, MDPI, vol. 13(7), pages 1-18, July.
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    74. Hautsch, Nikolaus, 2002. "Modelling Intraday Trading Activity Using Box-Cox-ACD Models," CoFE Discussion Papers 02/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    75. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    76. Aerambamoorthy Thavaneswaran & Nalini Ravishanker & You Liang, 2015. "Generalized duration models and optimal estimation using estimating functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 129-156, February.
    77. Yingjie Dong & Yiu-Kuen Tse, 2017. "Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility," Econometrics, MDPI, vol. 5(4), pages 1-19, November.
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  23. Fernandes, M., 2000. "Central Limit Theorem for Asymmetric Kernel Functionals," Economics Working Papers eco2000/1, European University Institute.

    Cited by:

    1. Matthias HAGMANN & Olivier SCAILLET, 2003. "Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators," FAME Research Paper Series rp91, International Center for Financial Asset Management and Engineering.
    2. Taoufik Bouezmarni & Jeroen V.K. Rombouts, 2006. "Nonparametric Density Estimation for Positive Time Series," Cahiers de recherche 06-09, HEC Montréal, Institut d'économie appliquée.
    3. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    4. Flôres Junior, Renato Galvão, 2004. "On the use (fulness) of CGE modelling in trade negotiations and policy," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 564, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    5. Marcelo Fernandes & Joachim Grammig, 2000. "Non-Parametric Specification Tests For Conditional Duration Models," Computing in Economics and Finance 2000 40, Society for Computational Economics.
    6. Nikolay Gospodinov & Masayuki Hirukawa, 2008. "Time Series Nonparametric Regression Using Asymmetric Kernels with an Application to Estimation of Scalar Diffusion Processes," CIRJE F-Series CIRJE-F-573, CIRJE, Faculty of Economics, University of Tokyo.
    7. Muhammad Hanif, 2011. "Reweighted Nadaraya-Watson estimator of scalar diffusion models by using asymmetric kernels," Far East Journal of Psychology and Business, Far East Research Centre, vol. 4(5), pages 53-69, July.
    8. Masayuki Hirukawa & Mari Sakudo, 2016. "Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels," Econometrics, MDPI, vol. 4(2), pages 1-27, June.
    9. Pierre Lafaye de Micheaux & Frédéric Ouimet, 2021. "A Study of Seven Asymmetric Kernels for the Estimation of Cumulative Distribution Functions," Mathematics, MDPI, vol. 9(20), pages 1-35, October.
    10. Marchant, Carolina & Bertin, Karine & Leiva, Víctor & Saulo, Helton, 2013. "Generalized Birnbaum–Saunders kernel density estimators and an analysis of financial data," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 1-15.
    11. Marcelo Fernandes & Eduardo Mendes & Olivier Scaillet, 2015. "Testing for symmetry and conditional symmetry using asymmetric kernels," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 649-671, August.
    12. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2013. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121, December.
    13. Bertin, Karine & Genest, Christian & Klutchnikoff, Nicolas & Ouimet, Frédéric, 2023. "Minimax properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    14. Ouimet, Frédéric, 2022. "A symmetric matrix-variate normal local approximation for the Wishart distribution and some applications," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    15. Mahdi Salehi & Andriette Bekker & Mohammad Arashi, 2023. "A Semi-parametric Density Estimation with Application in Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 52-78, April.
    16. Shunpu Zhang & Rohana Karunamuni, 2010. "Boundary performance of the beta kernel estimators," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(1), pages 81-104.

  24. Fernandes, M. & Grammig, J., 2000. "Non-Parametric Specification Tests for Conditional Duration Models," Economics Working Papers eco2000/4, European University Institute.

    Cited by:

    1. Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2000. "A Comparison of Financial Duration Models via Density Forecasts," Econometric Society World Congress 2000 Contributed Papers 0810, Econometric Society.
    2. Matthias HAGMANN & Olivier SCAILLET, 2003. "Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators," FAME Research Paper Series rp91, International Center for Financial Asset Management and Engineering.
    3. BAUWENS, Luc & HAUTSCH, Nikolaus, 2009. "Modelling financial high frequency data using point processes," LIDAM Reprints CORE 2123, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Taoufik Bouezmarni & Jeroen V.K. Rombouts, 2006. "Nonparametric Density Estimation for Positive Time Series," Cahiers de recherche 06-09, HEC Montréal, Institut d'économie appliquée.
    5. Marcelo Fernandes & Paulo Monteiro, 2005. "Central limit theorem for asymmetric kernel functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 425-442, September.
    6. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    7. Giuseppe Cavaliere & Indeewara Perera & Anders Rahbek, 2021. "Specification tests for GARCH processes," Discussion Papers 21-06, University of Copenhagen. Department of Economics.
    8. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    9. Anthony Tay & Christopher Ting & Yiu Kuen Tse & Mitch Warachka, 2007. "Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading," Finance Working Papers 22483, East Asian Bureau of Economic Research.
    10. Pipat Wongsaart & Jiti Gao, 2011. "Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 18/11, Monash University, Department of Econometrics and Business Statistics.
    11. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    12. Simos G. Meintanis & Bojana Milošević & Marko Obradović, 2020. "Goodness-of-fit tests in conditional duration models," Statistical Papers, Springer, vol. 61(1), pages 123-140, February.
    13. Petra Tomanová & Vladimír Holý, 2021. "Clustering of arrivals in queueing systems: autoregressive conditional duration approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 859-874, September.
    14. Fernandes, Marcelo & Grammig, Joachim, 2002. "A family of autoregressive conditional duration models," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 440, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    15. Xiaodong Jin & Janusz Kawczak, 2003. "Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 103-124, May.
    16. Lewbel, Arthur & Lu, Xun & Su, Liangjun, 2015. "Specification testing for transformation models with an application to generalized accelerated failure-time models," Journal of Econometrics, Elsevier, vol. 184(1), pages 81-96.
    17. Masayuki Hirukawa & Mari Sakudo, 2016. "Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels," Econometrics, MDPI, vol. 4(2), pages 1-27, June.
    18. Joao Amaro de Matos & Marcelo Fernandes, 2004. "Testing the Markov property with ultra-high frequency financial data," Nova SBE Working Paper Series wp462, Universidade Nova de Lisboa, Nova School of Business and Economics.
    19. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    20. Laurini, Márcio Poletti & Furlani, Luiz Gustavo Cassilatti & Portugal, Marcelo Savino, 2008. "Empirical market microstructure: An analysis of the BRL/US$ exchange rate market," Emerging Markets Review, Elsevier, vol. 9(4), pages 247-265, December.
    21. 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.
    22. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    23. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    24. Malec, Peter & Schienle, Melanie, 2012. "Nonparametric Kernel density estimation near the boundary," SFB 649 Discussion Papers 2012-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    25. Masayuki Hirukawa & Mari Sakudo, 2015. "Family of the generalised gamma kernels: a generator of asymmetric kernels for nonnegative data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(1), pages 41-63, March.
    26. Funke, Benedikt & Hirukawa, Masayuki, 2019. "Nonparametric estimation and testing on discontinuity of positive supported densities: a kernel truncation approach," Econometrics and Statistics, Elsevier, vol. 9(C), pages 156-170.
    27. Cavaliere, Giuseppe & Lu, Ye & Rahbek, Anders & Stærk-Østergaard, Jacob, 2023. "Bootstrap inference for Hawkes and general point processes," Journal of Econometrics, Elsevier, vol. 235(1), pages 133-165.
    28. 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.
    29. Marcelo Fernandes & Eduardo Mendes & Olivier Scaillet, 2015. "Testing for symmetry and conditional symmetry using asymmetric kernels," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 649-671, August.
    30. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2013. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121, December.
    31. 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.
    32. Amaro de Matos, Joao & Fernandes, Marcelo, 2007. "Testing the Markov property with high frequency data," Journal of Econometrics, Elsevier, vol. 141(1), pages 44-64, November.
    33. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    34. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    35. Guo, Bin & Li, Shuo, 2018. "Diagnostic checking of Markov multiplicative error models," Economics Letters, Elsevier, vol. 170(C), pages 139-142.
    36. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    37. Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    38. Wing Lon Ng, 2010. "Dynamic Order Submission And Herding Behavior In Electronic Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 27-43, March.
    39. 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.

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  5. Valentina Corradi & Walter Distaso & Marcelo Fernandes, 2020. "Testing for Jump Spillovers Without Testing for Jumps," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1214-1226, July.

    Cited by:

    1. Deniz Erdemlioglu & Christopher J. Neely & Xiye Yang, 2023. "Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications," Working Papers 2023-016, Federal Reserve Bank of St. Louis.

  6. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

    Cited by:

    1. Evangelos Salachas & Georgios P. Kouretas & Nikiforos T. Laopodis, 2024. "The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1018-1041, July.
    2. Jamie L. Cross & Aubrey Poon & Wenying Yao & Dan Zhu, 2024. "A Constrained Dynamic Nelson-Siegel Model for Monetary Policy Analysis," Working Papers No 06/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    4. Fernandes, Marcelo & Nunes, Clemens & Reis, Yuri, 2021. "What Drives the Nominal Yield Curve in Brazil?," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(2), April.
    5. Januj Amar Juneja, 2022. "A Computational Analysis of the Tradeoff in the Estimation of Different State Space Specifications of Continuous Time Affine Term Structure Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 173-220, June.
    6. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.

  7. Daniel Cerqueira & Danilo Coelho & Marcelo Fernandes & Jony Pinto Junior, 2018. "Guns and Suicides," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 289-294, July.

    Cited by:

    1. Daniel Cerqueira & Danilo Santa Cruz Coelho & John J. Donohue & Marcelo Fernandes & Jony Arrais Pinto Jr., 2019. "A Panel-based Proxy for Gun Prevalence in the US," NBER Working Papers 25530, National Bureau of Economic Research, Inc.

  8. Marcelo Fernandes & Cristina M. Scherrer, 2018. "Price discovery in dual‐class shares across multiple markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 129-155, January.
    See citations under working paper version above.
  9. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    See citations under working paper version above.
  10. Marcelo Fernandes & Marcelo C. Medeiros & Alvaro Veiga, 2016. "A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1221-1250, August.

    Cited by:

    1. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.

  11. Fernandes, Marcelo & Mergulhão, João, 2016. "Anticipatory effects in the FTSE 100 index revisions," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 79-90.
    See citations under working paper version above.
  12. Marcelo Fernandes & Eduardo Mendes & Olivier Scaillet, 2015. "Testing for symmetry and conditional symmetry using asymmetric kernels," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 649-671, August.
    See citations under working paper version above.
  13. Fernandes, Marcelo & Thiele, Eduardo, 2015. "The Macroeconomic Determinants of the Term Structure of Inflation Expectations in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.

    Cited by:

    1. Nunes, Clemens Vinicius & Doi, Jonas & Fernandes, Marcelo, 2017. "Disagreement in Inflation Forecasts and Inflation Risk Premia in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(1), May.
    2. Fernandes, Marcelo & Nunes, Clemens & Reis, Yuri, 2021. "What Drives the Nominal Yield Curve in Brazil?," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(2), April.
    3. Mauro Sayar Ferreira & Joice Marques Figueiredo, 2024. "The influence of global uncertainty and financial shocks, and sovereign risk shock on the Brazilian term structure of interest rate," Textos para Discussão Cedeplar-UFMG 674, Cedeplar, Universidade Federal de Minas Gerais.

  14. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    See citations under working paper version above.
  15. Fernandes, Marcelo & Nunes, Ricardo, 2014. "Brazilian Corporate Debt Issuance: Should You Invest in Local or International Bonds?," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.

    Cited by:

    1. Renu Kohli & Pravakar Sahoo & M. Shuheb Khan, 2017. "Developing India's Offshore Local Currency Bond Market: Lessons from Emerging Countries," Working Papers id:12039, eSocialSciences.

  16. Fernandes, Marcelo & Preumont, Pierre-Yves, 2012. "The Finite-Sample Size of the BDS Test for GARCH Standardized Residuals," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 32(2), April.
    See citations under working paper version above.
  17. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.

    Cited by:

    1. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    2. Evzen Kocenda & Vit Bubak & Filip Zikes, 2011. "Volatility Transmission in Emerging European Foreign Exchange Markets," William Davidson Institute Working Papers Series wp1020, William Davidson Institute at the University of Michigan.
    3. Wei-Zhen Li & Jin-Rui Zhai & Zhi-Qiang Jiang & Gang-Jin Wang & Wei-Xing Zhou, 2020. "Predicting tail events in a RIA-EVT-Copula framework," Papers 2004.03190, arXiv.org, revised Apr 2020.
    4. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2013. "A nonparametric test of a strong leverage hypothesis," CeMMAP working papers 28/13, Institute for Fiscal Studies.
    5. Su, Liangjun & White, Halbert, 2014. "Testing conditional independence via empirical likelihood," Journal of Econometrics, Elsevier, vol. 182(1), pages 27-44.
    6. Cardona, Laura & Gutiérrez, Marcela & Agudelo, Diego A., 2017. "Volatility transmission between US and Latin American stock markets: Testing the decoupling hypothesis," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 115-127.
    7. Gonzalez-Perez, Maria T., 2015. "Model-free volatility indexes in the financial literature: A review," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 141-159.
    8. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
    9. Rim Ammar Lamouchi & Ruba Khalid Shira, 2023. "Heterogeneous Behavior and Volatility Transmission in the Forex Market using High-Frequency Data," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(3), pages 1-3.
    10. Shafiullah, Muhammad & Senthilkumar, Arunachalam & Lucey, Brian M. & Naeem, Muhammad Abubakr, 2024. "Deciphering asymmetric spillovers in US industries: Insights from higher-order moments," Research in International Business and Finance, Elsevier, vol. 70(PA).
    11. Donggyu Kim & Minseog Oh & Yazhen Wang, 2022. "Conditional quantile analysis for realized GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 640-665, July.
    12. Afees A. Salisu & Kazeem Isah, 2017. "Modeling the spillovers between stock market and money market in Nigeria," Working Papers 023, Centre for Econometric and Allied Research, University of Ibadan.
    13. Ying-Ying Shen & Zhi-Qiang Jiang & Jun-Chao Ma & Gang-Jin Wang & Wei-Xing Zhou, 2022. "Sector connectedness in the Chinese stock markets," Empirical Economics, Springer, vol. 62(2), pages 825-852, February.
    14. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    15. Amaro de Matos, Joao & Fernandes, Marcelo, 2007. "Testing the Markov property with high frequency data," Journal of Econometrics, Elsevier, vol. 141(1), pages 44-64, November.
    16. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    17. Martins, Luis F. & Gabriel, Vasco J., 2014. "Modelling long run comovements in equity markets: A flexible approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 288-295.
    18. Lien, Donald & Lee, Geul & Yang, Li & Zhang, Yuyin, 2018. "Volatility spillovers among the U.S. and Asian stock markets: A comparison between the periods of Asian currency crisis and subprime credit crisis," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 187-201.
    19. Luo, Jiawen & Wang, Shengquan, 2019. "The asymmetric high-frequency volatility transmission across international stock markets," Finance Research Letters, Elsevier, vol. 31(C), pages 104-109.
    20. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.

  18. Marcelo Fernandes & Breno Neri, 2010. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 276-306.
    See citations under working paper version above.
  19. Amaro de Matos, Joao & Fernandes, Marcelo, 2007. "Testing the Markov property with high frequency data," Journal of Econometrics, Elsevier, vol. 141(1), pages 44-64, November.

    Cited by:

    1. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    2. Su, Liangjun & White, Halbert, 2014. "Testing conditional independence via empirical likelihood," Journal of Econometrics, Elsevier, vol. 182(1), pages 27-44.
    3. Laurini, Márcio Poletti & Furlani, Luiz Gustavo Cassilatti & Portugal, Marcelo Savino, 2008. "Empirical market microstructure: An analysis of the BRL/US$ exchange rate market," Emerging Markets Review, Elsevier, vol. 9(4), pages 247-265, December.
    4. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.

  20. Fernandes, Marcelo & Linton, Oliver & Scaillet, Olivier, 2007. "Semiparametric methods in econometrics," Journal of Econometrics, Elsevier, vol. 141(1), pages 1-4, November.

    Cited by:

    1. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.

  21. Fernandes, Marcelo, 2006. "Financial crashes as endogenous jumps: estimation, testing and forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 30(1), pages 111-141, January.

    Cited by:

    1. B. Craven & Sardar Islam, 2008. "A model for stock market returns: non-Gaussian fluctuations and financial factors," Review of Quantitative Finance and Accounting, Springer, vol. 30(4), pages 355-370, May.
    2. Ruijun Bu & Fredj Jawadi & Yuyi Li, 2020. "A multifactor transformed diffusion model with applications to VIX and VIX futures," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 27-53, January.
    3. Xibin Zhang & Robert D. Brooks & Maxwell L. King, 2007. "A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation," Monash Econometrics and Business Statistics Working Papers 11/07, Monash University, Department of Econometrics and Business Statistics.
    4. Li, Haiqi & Kim, Myeong Jun & Park, Sung Y., 2016. "Nonlinear relationship between crude oil price and net futures positions: A dynamic conditional distribution approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 217-225.
    5. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    6. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    7. Li, Minqiang, 2010. "A damped diffusion framework for financial modeling and closed-form maximum likelihood estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 132-157, February.

  22. Fernandes, Marcelo & Grammig, Joachim, 2006. "A family of autoregressive conditional duration models," Journal of Econometrics, Elsevier, vol. 130(1), pages 1-23, January.
    See citations under working paper version above.
  23. Marcelo Fernandes & Paulo Monteiro, 2005. "Central limit theorem for asymmetric kernel functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 425-442, September.
    See citations under working paper version above.
  24. Fernandes, Marcelo & Toro, Juan, 2005. "O Mecanismo de Transmissão Monetária na Economia Brasileira Pós-Plano Real," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 59(1), January.

    Cited by:

    1. Brisne J. V. Céspedes & Elcyon C. R. Lima & Alexis Maka, 2005. "Monetary Policy, Inflation and the Level of Economic Activity in Brasil After the Real Plan: Stylized Facts From SVAR Models," Discussion Papers 1101, Instituto de Pesquisa Econômica Aplicada - IPEA.
    2. Marco A. F. H. Cavalcanti & Luciano Vereda, 2011. "Propriedades Dinâmicas de Um Modelo DSGE Com Parametrizações Alternativas Para o Brasil," Discussion Papers 1588, Instituto de Pesquisa Econômica Aplicada - IPEA.
    3. Igor Ézio Maciel Silva & Nelson Leitão Paes & Jocildo Fernandes Bezerra, 2016. "Evidences Of Incomplete Interest Rate Pass-Through, Directed Credit And Cost Channel Of Monetary Policy In Brazil," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 036, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Luciano Vereda & Marco A. F. H. Cavalcanti, 2010. "Modelo Dinâmico Estocástico de Equilíbrio Geral (DSGE) Para a Economia Brasileira: Versão 1," Discussion Papers 1479, Instituto de Pesquisa Econômica Aplicada - IPEA.

  25. Fernandes, Marcelo & de Sa Mota, Bernardo & Rocha, Guilherme, 2005. "A multivariate conditional autoregressive range model," Economics Letters, Elsevier, vol. 86(3), pages 435-440, March.

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
    3. He, Angela W.W. & Kwok, Jerry T.K. & Wan, Alan T.K., 2010. "An empirical model of daily highs and lows of West Texas Intermediate crude oil prices," Energy Economics, Elsevier, vol. 32(6), pages 1499-1506, November.
    4. Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
    5. 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.
    6. Yan-Leung Cheung & Yin-Wong Cheung & Alan T.K. Wan, 2008. "A High-Low Model of Daily Stock Price Ranges," CESifo Working Paper Series 2387, CESifo.
    7. Piotr Fiszeder, 2018. "Low and high prices can improve covariance forecasts: The evidence based on currency rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 641-649, September.
    8. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
    9. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    10. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    11. Lakshmi Padmakumari & S. Maheswaran, 2018. "Covariance estimation using random permutations," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    12. Lee, O. & Shin, D.W., 2008. "Geometric ergodicity and [beta]-mixing property for a multivariate CARR model," Economics Letters, Elsevier, vol. 100(1), pages 111-114, July.
    13. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
    14. Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard, 2012. "Forecasting volatility with asymmetric smooth transition dynamic range models," International Journal of Forecasting, Elsevier, vol. 28(2), pages 384-399.
    15. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    16. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

  26. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    See citations under working paper version above.
  27. Mota, Bernardo de Sá & Fernandes, Marcelo, 2004. "Desempenho de Estimadores de Volatilidade na Bolsa de Valores de São Paulo," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 58(3), July.
    See citations under working paper version above.
  28. Fernandes, Marcelo, 2004. "Bounds for the probability distribution function of the linear ACD process," Statistics & Probability Letters, Elsevier, vol. 68(2), pages 169-176, June.
    See citations under working paper version above.
  29. Marcelo Fernandes & Marco Aurélio Dos Santos Rocha, 0. "Are price limits on futures markets that cool? Evidence from the Brazilian Mercantile and Futures Exchange," Journal of Financial Econometrics, Oxford University Press, vol. 5(2), pages 219-242.
    See citations under working paper version above.
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