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Realized volatility forecasting: empirical evidence from stock market indices and exchange rates

  • Linlan Xiao
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    This study evaluates the performance of four models for predicting daily realized volatility of S&P 500 index (SPX), Dow Jones Industry Average (DJIA), Canadian dollar (CAD/USD) and British Pound (USD/GBP) exchange rates. The competing models include a Simple Regression Model (SRM), Stochastic Volatility model with Lagged inter-temporal dependence (SVL), Stochastic Volatility model with Contemporaneous dependence (SVC), and a Heterogeneous Autoregressive (HAR) model. The main purpose is to examine whether allowing asymmetric relationships between return and volatility and leptokurtosis, or modelling the long-memory behaviour of volatility, would result in an improvement in forecast accuracy. Different approaches are considered when constructing daily realized volatility. Employing realized volatility in the in-sample estimation, the procedure is straightforward. The famous Diebold and Mariano's (1995) robust tests are applied to investigate whether the competing models provide equally accurate forecasts. Four different measures are used to evaluate the forecasting accuracy. The results suggest that allowing asymmetric behaviour and leptokurtosis do not seem to improve point forecasts, whereas modelling long-memory behaviour seems to.

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    File URL: http://hdl.handle.net/10.1080/09603107.2012.707769
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    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 23 (2013)
    Issue (Month): 1 (January)
    Pages: 57-69

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    Handle: RePEc:taf:apfiec:v:23:y:2013:i:1:p:57-69
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    1. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
    3. 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.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. Martin Martens, 2002. "Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(6), pages 497-518, 06.
    6. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    7. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    8. Fulvio Corsi & Stefan Mittnik & Christian Pigorsch & Uta Pigorsch, 2008. "The Volatility of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 46-78.
    9. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
    10. Jun Yu, 2002. "Forecasting volatility in the New Zealand stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 12(3), pages 193-202.
    11. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    12. Jose A. Lopez, 1995. "Evaluating the predictive accuracy of volatility models," Research Paper 9524, Federal Reserve Bank of New York.
    13. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
    14. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-061, New York University, Leonard N. Stern School of Business-.
    15. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    16. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
    17. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
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