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Volatility Forecasting With Range-Based EGARCH Models

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Cited by:

  1. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
  2. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
  3. Klein, Tony & Walther, Thomas, 2017. "Fast fractional differencing in modeling long memory of conditional variance for high-frequency data," Finance Research Letters, Elsevier, vol. 22(C), pages 274-279.
  4. Ray Chou & Chun-Chou Wu & Nathan Liu, 2009. "Forecasting time-varying covariance with a range-based dynamic conditional correlation model," Review of Quantitative Finance and Accounting, Springer, vol. 33(4), pages 327-345, November.
  5. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
  6. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
  7. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
  8. (Jeremy) Chiu, Ching-wai & Harris, Richard D.F. & Stoja, Evarist & Chin, Michael, 2018. "Financial market Volatility, macroeconomic fundamentals and investor Sentiment," Journal of Banking & Finance, Elsevier, vol. 92(C), pages 130-145.
  9. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
  10. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
  11. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2019. "Range-based DCC models for covariance and value-at-risk forecasting," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 58-76.
  12. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
  13. Kumar, Dilip, 2015. "Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis," Economic Modelling, Elsevier, vol. 49(C), pages 354-371.
  14. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
  15. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.
  16. Dilip Kumar, 2016. "Sudden changes in crude oil price volatility: an application of extreme value volatility estimator," American Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 4(3/4), pages 215-234.
  17. Léo Parent, 2022. "The EWMA Heston model," Post-Print hal-04431111, HAL.
  18. Subrata ROY, 2021. "Volatility Forecasting, Market Efficiency and Effect of Recession of SRI Indices," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(627), S), pages 259-284, Summer.
  19. S. Bhaumik & M. Karanasos & A. Kartsaklas, 2008. "Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market," William Davidson Institute Working Papers Series wp935, William Davidson Institute at the University of Michigan.
  20. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
  21. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, January.
  22. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723, October.
  23. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
  24. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
  25. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
  26. Simón Sosvilla-Rivero & Amalia Morales-Zumaquero, 2011. "Volatility in EMU sovereign bond yields: Permanent and transitory components," Working Papers del Instituto Complutense de Estudios Internacionales 1106, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
  27. Yuta Kurose, 2022. "Bayesian GARCH modeling for return and range," Economics Bulletin, AccessEcon, vol. 42(3), pages 1717-1727.
  28. Harris, Richard D.F. & Stoja, Evarist & Yilmaz, Fatih, 2011. "A cyclical model of exchange rate volatility," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3055-3064, November.
  29. Yang, Hu & Chen, Yu & Chen, Kedong & Wang, Haijun, 2024. "Temporal-spatial dependencies enhanced deep learning model for time series forecast," International Review of Financial Analysis, Elsevier, vol. 94(C).
  30. Olivier Wintenberger, 2013. "Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 846-867, December.
  31. Paulo M.M. Rodrigues & Matei Demetrescu, 2018. "Testing the fractionally integrated hypothesis using M estimation: With an application to stock market volatility," Working Papers w201817, Banco de Portugal, Economics and Research Department.
  32. Lakshmi Padmakumari & S Maheswaran, 2016. "A Regression Based Approach to Capturing the Level Dependence in the Volatility of Stock Returns," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(12), pages 706-718, December.
  33. Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
  34. Christoffersen, Peter & Fournier, Mathieu & Jacobs, Kris & Karoui, Mehdi, 2021. "Option-Based Estimation of the Price of Coskewness and Cokurtosis Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(1), pages 65-91, February.
  35. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
  36. 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.
  37. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
  38. Vladimir Tsenkov, 2009. "Financial Markets Modelling," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 87-96.
  39. Chou, Ray Yeutien & Cai, Yijie, 2009. "Range-based multivariate volatility model with double smooth transition in conditional correlation," Global Finance Journal, Elsevier, vol. 20(2), pages 137-152.
  40. Wang, Yi-Chiuan & Wu, Jyh-Lin & Lai, Yi-Hao, 2018. "New evidence on asymmetric return–volume dependence and extreme movements," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 212-227.
  41. Gianluca Vagnani, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Post-Print hal-00736952, HAL.
  42. Ozgur (Ozzy) Akay & Mark D. Griffiths & Drew B. Winters, 2010. "On The Robustness Of Range‐Based Volatility Estimators," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 179-199, June.
  43. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
  44. Wintenberger, Olivier & Cai, Sixiang, 2011. "Parametric inference and forecasting in continuously invertible volatility models," MPRA Paper 31767, University Library of Munich, Germany.
  45. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
  46. Sin, Chor-Yiu (CY), 2013. "Using CARRX models to study factors affecting the volatilities of Asian equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 552-564.
  47. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
  48. Nicole Abruzzo & Yang-Ho Park, 2014. "An Empirical Analysis of Futures Margin Changes: Determinants and Policy Implications," Finance and Economics Discussion Series 2014-86, Board of Governors of the Federal Reserve System (U.S.).
  49. Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
  50. Kumar, Dilip & Maheswaran, S., 2014. "Modeling and forecasting the additive bias corrected extreme value volatility estimator," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 166-176.
  51. Petmezas, Dimitris & Santamaria, Daniel, 2014. "Investor induced contagion during the banking and European sovereign debt crisis of 2007–2012: Wealth effect or portfolio rebalancing?," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 401-424.
  52. 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.
  53. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England working papers 660, Bank of England.
  54. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
  55. Prelorentzos, Arsenios-Georgios N. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Xidonas, Panos & Goutte, Stephane & Thomakos, Dimitrios D., 2024. "Introducing the GVAR-GARCH model: Evidence from financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
  56. Kim Karlsson, Hyunjoo & Li, Yushu, 2024. "Investigation of Swedish krona exchange rate volatility by APARCH-Support Vector Regression," Working Papers in Economics and Statistics 10/2024, Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
  57. Haase, Marco & Huss, Matthias, 2018. "Guilty speculators? Range-based conditional volatility in a cross-section of wheat futures," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 29-46.
  58. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
  59. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
  60. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
  61. Bernard Ben Sita, 2019. "Crude oil and gasoline volatility risk into a Realized-EGARCH model," Review of Quantitative Finance and Accounting, Springer, vol. 53(3), pages 701-720, October.
  62. 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.
  63. Reboredo, Juan C. & Ugando, Mikel, 2015. "Downside risks in EU carbon and fossil fuel markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 111(C), pages 17-35.
  64. Lucas Hafemann, 2021. "The Nexus between lockdown Shocks and Economic Uncertainty: Empirical Evidence from a VAR model," MAGKS Papers on Economics 202132, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  65. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
  66. 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.
  67. Futeri Jazeilya Md Fadzil & John G. O’Hara & Wing Lon Ng, 2017. "Cross-sectional volatility index as a proxy for the VIX in an Asian market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1364011-136, January.
  68. 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.
  69. Md. Mohibul Islam & Anisul M. Islam, 2017. "Impact of Index Options on Emerging Market Volatility: The Case of the Malaysian Equity Market," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 3(9), pages 157-15-172, 09-2017.
  70. Wu, Chih-Chiang & Chiu, Junmao, 2017. "Economic evaluation of asymmetric and price range information in gold and general financial markets," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 53-68.
  71. Molnár, Peter, 2012. "Properties of range-based volatility estimators," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 20-29.
  72. Thuy Thi Thu Truong & Jungmu Kim, 2019. "Premiums for Non-Sustainable and Sustainable Components of Market Volatility: Evidence from the Korean Stock Market," Sustainability, MDPI, vol. 11(18), pages 1-15, September.
  73. Ping-Chen Tsai & Chi-Ming Tsai, 2021. "Estimating the proportion of informed and speculative traders in financial markets: evidence from exchange rate," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 443-470, July.
  74. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "A No Arbitrage Fractional Cointegration Analysis Of The Range Based Volatility," CREATES Research Papers 2009-31, Department of Economics and Business Economics, Aarhus University.
  75. Shaen Corbet & Cian Twomey, 2014. "Have Exchange Traded Funds Influenced Commodity Market Volatility?," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 323-335.
  76. 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.
  77. Tomasz Skoczylas, 2013. "Modelowanie i prognozowanie zmienności przy użyciu modeli opartych o zakres wahań," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 35.
  78. Sensoy, Ahmet & Uzun, Sevcan & Lucey, Brian M., 2021. "Commonality in FX liquidity: High-frequency evidence," Finance Research Letters, Elsevier, vol. 39(C).
  79. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany.
  80. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
  81. Tomasz Skoczylas, 2015. "Bivariate GARCH models for single asset returns," Working Papers 2015-03, Faculty of Economic Sciences, University of Warsaw.
  82. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
  83. Padmakumari, Lakshmi & S., Maheswaran, 2017. "A new statistic to capture the level dependence in stock price volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 355-362.
  84. Wang, Weichen & An, Ran & Zhu, Ziwei, 2024. "Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective," Journal of Econometrics, Elsevier, vol. 239(2).
  85. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
  86. Kumar, Dilip & Maheswaran, S., 2014. "A new approach to model and forecast volatility based on extreme value of asset prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 128-140.
  87. Grosche, Stephanie & Heckelei, Thomas, 2014. "Directional Volatility Spillovers between Agricultural, Crude Oil, Real Estate and other Financial Markets," Discussion Papers 166079, University of Bonn, Institute for Food and Resource Economics.
  88. Miao, Daniel Wei-Chung & Wu, Chun-Chou & Su, Yi-Kai, 2013. "Regime-switching in volatility and correlation structure using range-based models with Markov-switching," Economic Modelling, Elsevier, vol. 31(C), pages 87-93.
  89. 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.
  90. Chen, Cathy W.S. & Gerlach, Richard & Lin, Edward M.H., 2008. "Volatility forecasting using threshold heteroskedastic models of the intra-day range," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2990-3010, February.
  91. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
  92. Chih-Wen Hsiao & Ya-Chuan Chan & Mei-Yu Lee & Hsi-Peng Lu, 2021. "Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
  93. Vagnani, Gianluca, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 103-118, October.
  94. Tomasz Skoczylas, 2015. "Log-volatility enhanced GARCH models for single asset returns," Bank i Kredyt, Narodowy Bank Polski, vol. 46(5), pages 411-432.
  95. Jie Zhu, 2009. "Pricing volatility of stock returns with volatile and persistent components," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(3), pages 243-269, September.
  96. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
  97. Ng, Kok Haur & Peiris, Shelton & Chan, Jennifer So-kuen & Allen, David & Ng, Kooi Huat, 2017. "Efficient modelling and forecasting with range based volatility models and its application," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 448-460.
  98. Jie Zhu, 2008. "Pricing Volatility of Stock Returns with Volatile and Persistent Components," CREATES Research Papers 2008-14, Department of Economics and Business Economics, Aarhus University.
  99. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
  100. 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).
  101. Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.
  102. Weijia Peng & Chun Yao, 2022. "Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence," JRFM, MDPI, vol. 15(8), pages 1-21, July.
  103. Zaremba, Adam, 2019. "Price range and the cross-section of expected country and industry returns," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 174-189.
  104. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
  105. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
  106. Dilip Kumar, 2018. "Modeling and Forecasting Unbiased Extreme Value Volatility Estimator in Presence of Leverage Effect," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 313-335, June.
  107. Bhaumik, S. & Karanasos, M. & Kartsaklas, A., 2016. "The informative role of trading volume in an expanding spot and futures market," Journal of Multinational Financial Management, Elsevier, vol. 35(C), pages 24-40.
  108. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
  109. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
  110. Haibin Xie & Shouyang Wang, 2018. "Timing the market: the economic value of price extremes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-24, December.
  111. Chiang, Min-Hsien & Wang, Li-Min, 2011. "Volatility contagion: A range-based volatility approach," Journal of Econometrics, Elsevier, vol. 165(2), pages 175-189.
  112. Vipul Kumar Singh, 2013. "Effectiveness of volatility models in option pricing: evidence from recent financial upheavals," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 10(3), pages 352-375, October.
  113. Peter Molnár, 2016. "High-low range in GARCH models of stock return volatility," Applied Economics, Taylor & Francis Journals, vol. 48(51), pages 4977-4991, November.
  114. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
  115. Isuru Ratnayake & V. A. Samaranayake, 2022. "Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model," Papers 2202.03351, arXiv.org, revised Mar 2022.
  116. Xie, Haibin & Wu, Xinyu, 2017. "A conditional autoregressive range model with gamma distribution for financial volatility modelling," Economic Modelling, Elsevier, vol. 64(C), pages 349-356.
  117. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.
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