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Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

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Cited by:

  1. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  2. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
  3. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.
  4. Zhongjun Qu, 2011. "A Test Against Spurious Long Memory," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 423-438, July.
  5. Bollerslev, Tim & Kretschmer, Uta & Pigorsch, Christian & Tauchen, George, 2009. "A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects," Journal of Econometrics, Elsevier, vol. 150(2), pages 151-166, June.
  6. Kruse, Robinson & Leschinski, Christian & Will, Michael, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," Hannover Economic Papers (HEP) dp-571, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  7. Samit Paul & Madhusudan Karmakar, 2017. "Relative Efficiency of Component GARCH-EVT Approach in Managing Intraday Market Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 21(4), pages 247-283, December.
  8. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
  9. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
  10. Wen Cheong Chin & Min Cherng Lee, 2018. "S&P500 volatility analysis using high-frequency multipower variation volatility proxies," Empirical Economics, Springer, vol. 54(3), pages 1297-1318, May.
  11. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
  12. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
  13. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
  14. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
  15. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
  16. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
  17. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
  18. Ruiz Esther & Pérez Ana, 2012. "Maximally Autocorrelated Power Transformations: A Closer Look at the Properties of Stochastic Volatility Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-33, September.
  19. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
  20. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
  21. Filip Žikeš & Jozef Baruník & Nikhil Shenai, 2017. "Modeling and forecasting persistent financial durations," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1081-1110, November.
  22. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  23. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
  24. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
  25. Xiang, Ju & Zhu, Xiaoneng, 2014. "Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 134-148.
  26. Offer Lieberman & Peter Phillips, 2008. "Refined Inference on Long Memory in Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 254-267.
  27. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
  28. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
  29. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
  30. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
  31. Aue, Alexander & Horváth, Lajos & Hurvich, Clifford & Soulier, Philippe, 2014. "Limit Laws In Transaction-Level Asset Price Models," Econometric Theory, Cambridge University Press, vol. 30(3), pages 536-579, June.
  32. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
  33. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
  34. Anders Eriksson & Daniel P. A. Preve & Jun Yu, 2019. "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model," JRFM, MDPI, vol. 12(3), pages 1-23, August.
  35. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
  36. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
  37. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
  38. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
  39. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 29-56, Winter.
  40. Zhang, Hanyu & Dufour, Alfonso, 2019. "Modeling intraday volatility of European bond markets: A data filtering application," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 131-146.
  41. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
  42. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
  43. Macaro, Christian, 2010. "Bayesian non-parametric signal extraction for Gaussian time series," Journal of Econometrics, Elsevier, vol. 157(2), pages 381-395, August.
  44. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
  45. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723.
  46. Rebecca J. Sela & Clifford M. Hurvich, 2009. "Computationally efficient methods for two multivariate fractionally integrated models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 631-651, November.
  47. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
  48. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
  49. 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.
  50. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  51. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
  52. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
  53. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2016. "Common trends in global volatility," Journal of International Money and Finance, Elsevier, vol. 67(C), pages 194-214.
  54. Jiang, George J. & Tian, Yisong S., 2010. "Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 503-533, April.
  55. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
  56. Ferraz, Rosemeire O. & Hotta, Luiz K., 2007. "Quasi-Maximum Likelihood Estimation of Long-Memory Stochastic Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(2), November.
  57. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
  58. Degiannakis, Stavros, 2018. "Multiple days ahead realized volatility forecasting: Single, combined and average forecasts," Global Finance Journal, Elsevier, vol. 36(C), pages 41-61.
  59. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
  60. Jian Zhou, 2017. "Forecasting REIT volatility with high-frequency data: a comparison of alternative methods," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2590-2605, June.
  61. Grace Yap & Wen Cheong Chin, 2016. "Spectral bandwidth selection for long memory," Modern Applied Science, Canadian Center of Science and Education, vol. 10(8), pages 1-63, August.
  62. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
  63. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
  64. Dumitru, Ana-Maria & Hizmeri, Rodrigo & Izzeldin, Marwan, 2019. "Forecasting the Realized Variance in the Presence of Intraday Periodicity," EconStor Preprints 193631, ZBW - Leibniz Information Centre for Economics.
  65. Thibault Vatter & Hau-Tieng Wu & Valérie Chavez-Demoulin & Bin Yu, 2015. "Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality," Econometrics, MDPI, vol. 3(4), pages 1-24, December.
  66. 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.
  67. Rohit Deo & Clifford Hurvich & Philippe Soulier & Yi Wang, 2005. "Propagation of Memory Parameter from Durations to Counts," Econometrics 0511010, University Library of Munich, Germany.
  68. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
  69. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
  70. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
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