Forecasting Stock Market Realized Volatility using decomposition
Keywords:
stock market, realized volatility, high-frequency data, HAR model, variance decomposition, volatility forecasting, jumps.
Abstract
Empirical studies concerned with realized volatility reveal the presence of heterogeneous behavior within the stock market. The sum of this heterogeneous behavior takes a persistent form, which may be modeled and forecasted according to different time horizons by the class of HAR models. In this paper, we investigate the HAR-RV and HAR-RV-CJ models for high-frequency data based on five realized volatility indices. The aim here is to demonstrate that the predictability of realized volatility can be improved by decomposing realized variance into its continuous and jump components. What is more, the results show that this decomposition of the realized variance into its components does indeed enhance the modeling and forecasting of the indices’ realized volatility.
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References
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Lanne, Markku. 2007. "Forecasting realized exchange rate volatility by decomposition." International Journal of Forecasting no. 23 (2):307-320.
Martens, Martin. 2001. "Forecasting daily exchange rate volatility using intraday returns." Journal of International Money and Finance no. 20 (1):1-23.
Müller, Ulrich A., Michel M. Dacorogna, Rakhal D. Davé, Richard B. Olsen, Olivier V. Pictet, and Jacob E. von Weizsäcker. 1997. "Volatilities of different time resolutions — Analyzing the dynamics of market components." Journal of Empirical Finance no. 4 (2):213-239.
Pooter, Michiel de, Martin Martens, and Dick van Dijk. 2008. "Predicting the daily covariance matrix for s&p 100 stocks using intraday data—but which frequency to use?" Econometric Reviews no. 27 (1-3):199-229.
Andersen, Torben G, Tim Bollerslev, and 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 no. 89 (4):701-720.
Andersen, Torben G, Tim Bollerslev, Francis X Diebold, and Paul Labys. 2003. "Modeling and forecasting realized volatility." Econometrica no. 71 (2):579-625.
Andersen, Torben G, Tim Bollerslev, and Xin Huang. 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures." Journal of Econometrics no. 160 (1):176-189.
Barndorff-Nielsen, Ole E, and Neil Shephard. 2004. "Power and bipower variation with stochastic volatility and jumps." Journal of financial econometrics no. 2 (1):1-37.
Barndorff-Nielsen, Ole E, and Neil Shephard. 2006. "Econometrics of testing for jumps in financial economics using bipower variation." Journal of financial Econometrics no. 4 (1):1-30.
Barunik, Jozef, Tomas Krehlik, and Lukas Vacha. 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain." European Journal of Operational Research no. 251 (1):329-340.
Beine, Michel, Jérôme Lahaye, Sébastien Laurent, Christopher J Neely, and Franz C Palm. 2007. "Central bank intervention and exchange rate volatility, its continuous and jump components." International journal of finance & economics no. 12 (2):201-223.
Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. Handbook of econometrics, 4, 2959-3038.
Bollerslev, Tim, Uta Kretschmer, Christian Pigorsch, and George Tauchen. 2009. "A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects." Journal of Econometrics no. 150 (2):151-166.
Busch, T., Christensen, B. J., & Nielsen, M. Ø. (2011). The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics, 160(1), 48-57.
Corsi, Fulvio. 2009. "A Simple Approximate Long-Memory Model of Realized Volatility." Journal of Financial Econometrics no. 7 (2):174-196.
Corsi Fulvio, Davide Pirino, and Roberto Reno. 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting." Journal of Econometrics 159 (2): 276-288.
Degiannakis*, S. (2004). Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model. Applied Financial Economics, 14(18), 1333-1342.
Degiannakis, S. A. (2008). Forecasting Vix. Journal of Money, Investment and Banking, (4).
Fernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10.
French, Kenneth R, G William Schwert, and Robert F Stambaugh. 1987. "Expected stock returns and volatility." Journal of financial Economics no. 19 (1):3-29.
Fuentes, M. A., Gerig, A., & Vicente, J. (2009). Universal behavior of extreme price movements in stock markets. PloS one, 4(12).
Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20(7), 873-889.
Huang, Chuangxia, Xu Gong, Xiaohong Chen, and Fenghua Wen. 2013. "Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model." In Abstract and Applied Analysis, vol.2013. Hindawi.
Koopman, Siem Jan, Borus Jungbacker, and Eugenie Hol. 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements." Journal of Empirical Finance no. 12 (3):445-475.
Kumar Manish. 2010. "Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component." Journal of Industrial Engineering and Management (JIEM) 3 (1): 199-220.
Lanne, Markku. 2007. "Forecasting realized exchange rate volatility by decomposition." International Journal of Forecasting no. 23 (2):307-320.
Martens, Martin. 2001. "Forecasting daily exchange rate volatility using intraday returns." Journal of International Money and Finance no. 20 (1):1-23.
Müller, Ulrich A., Michel M. Dacorogna, Rakhal D. Davé, Richard B. Olsen, Olivier V. Pictet, and Jacob E. von Weizsäcker. 1997. "Volatilities of different time resolutions — Analyzing the dynamics of market components." Journal of Empirical Finance no. 4 (2):213-239.
Pooter, Michiel de, Martin Martens, and Dick van Dijk. 2008. "Predicting the daily covariance matrix for s&p 100 stocks using intraday data—but which frequency to use?" Econometric Reviews no. 27 (1-3):199-229.
Published
2020-04-18
How to Cite
LAMOUCHI, R. (2020). Forecasting Stock Market Realized Volatility using decomposition. Journal of Research in Business, Economics and Management, 14(3), 2661-2675. Retrieved from http://scitecresearch.com/journals/index.php/jrbem/article/view/1855
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