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Examination on the Relationship Between VHSI, HSI and Future Realized Volatility With Kalman Filter

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  • Yanhui Chen

    (City University of Hong Kong)

  • Kin Keung Lai

    (City University of Hong Kong)

Abstract

Hang Seng Index Volatility (VHSI) is a new barometer to research the variance of Hang Seng Index (HSI). This paper first explores how VHSI changes are influenced by HSI returns dynamically. The time-varying coefficients achieved by Kalman filter indicate a negative and asymmetric contemporaneous relationship between VHSI changes and HSI returns. More importantly, we find that this asymmetric effect is stronger in Hong Kong than that in US since the investors are more sensitive to negative returns. Second, this paper studies the relationship between VHSI and the future realized volatility of HSI, and predicts the future realized volatility of HSI with Kalman filter. The empirical findings suggest that VHSI is an unbiased and efficient estimate of the future realized volatility and includes information of the future realized volatility when employing monthly data. In addition, the predication performance of Kalman filter is better than linear regression model.

Suggested Citation

  • Yanhui Chen & Kin Keung Lai, 2013. "Examination on the Relationship Between VHSI, HSI and Future Realized Volatility With Kalman Filter," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(2), pages 200-216, December.
    • Handle: RePEc:spr:eurasi:v:3:y:2013:i:2:d:10.14208_ebr.2013.03.02.005
    DOI: 10.14208/ebr.2013.03.02.005
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    1. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    2. Becker, Ralf & Clements, Adam E., 2008. "Are combination forecasts of S&P 500 volatility statistically superior?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 122-133.
    3. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    4. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    5. Charlotte Strunk Hansen, 2001. "The relation between implied and realised volatility in the Danish option and equity markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 41(3), pages 197-228, November.
    6. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    7. Guilkey, David K. & Schmidt, Peter, 1989. "Extended tabulations for Dickey-Fuller tests," Economics Letters, Elsevier, vol. 31(4), pages 355-357, December.
    8. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    9. Charles J. Corrado & Thomas W. Miller, Jr., 2005. "The forecast quality of CBOE implied volatility indexes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 339-373, April.
    10. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    11. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    12. GIOT, Pierre, 2005. "Implied volatility indexes and daily Value at Risk models," LIDAM Reprints CORE 1840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Mihaela Manoliu & Stathis Tompaidis, 2002. "Energy futures prices: term structure models with Kalman filter estimation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 21-43.
    14. Bedendo, Mascia & Hodges, Stewart D., 2009. "The dynamics of the volatility skew: A Kalman filter approach," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1156-1165, June.
    15. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
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    6. Yam Wing Siu, 2018. "Volatility Forecast by Volatility Index and Its Use as a Risk Management Tool Under a Value-at-Risk Approach," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-48, June.
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