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Stock Market Volatility And The Forecasting Accuracy Of Implied Volatility Indices

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  • Kazuhiko NISHINA

    (Graduate School of Economics, Osaka University)

  • Tatsuro Nabil MAGHREBI

    ()
    (Faculty of Economics, Wakayama University)

  • Moo-Sung KIM

    (College of Business Administration, Pusan National University)

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    Abstract

    This study develops a new model-free benchmark of implied volatility for the Japanese stock market similar in construction to the new VIX based on the S&P 500 index. It also examines the stochastic dynamics of the implied volatility index and its relationship with realized volatility in both markets. There is evidence that implied volatility is governed by a long-memory process. Despite its upward bias, implied volatility is more reflective of changes in realized volatility than alternative GARCH models, which account for volatility persistence and the asymmetric impact of news. The implied volatility index is also found to be inclusive of some but not all information on future volatility contained in historical returns. However, its higher out-of sample performance provides further support to the rationale behind drawing inference about future stock market volatility based on the incremental information contained in options prices.

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    File URL: http://www2.econ.osaka-u.ac.jp/library/global/dp/0609.pdf
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    Bibliographic Info

    Paper provided by Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP) in its series Discussion Papers in Economics and Business with number 06-09.

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    Length: 33 pages
    Date of creation: Mar 2006
    Date of revision:
    Handle: RePEc:osk:wpaper:0609

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    Web page: http://www.econ.osaka-u.ac.jp/
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    Related research

    Keywords: Licensing; Implied volatility index; Out-of-sample forecasting; GARCH modelling;

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    1. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-81.
    2. Corrado, Charles J. & Miller, Thomas Jr., 1996. "A note on a simple, accurate formula to compute implied standard deviations," Journal of Banking & Finance, Elsevier, Elsevier, vol. 20(3), pages 595-603, April.
    3. Chambers, Donald R & Nawalkha, Sanjay K, 2001. "An Improved Approach to Computing Implied Volatility," The Financial Review, Eastern Finance Association, Eastern Finance Association, vol. 36(3), pages 89-99, August.
    4. Yacine Aït-Sahalia & Andrew W. Lo, 1998. "Nonparametric Estimation of State-Price Densities Implicit in Financial Asset Prices," Journal of Finance, American Finance Association, American Finance Association, vol. 53(2), pages 499-547, 04.
    5. Ball, Clifford A. & Roma, Antonio, 1994. "Stochastic Volatility Option Pricing," Journal of Financial and Quantitative Analysis, Cambridge University Press, Cambridge University Press, vol. 29(04), pages 589-607, December.
    6. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, Elsevier, vol. 105(1), pages 5-26, November.
    7. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 50(2), pages 125-150, November.
    8. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
    9. Amin, Kaushik I & Ng, Victor K, 1997. "Inferring Future Volatility from the Information in Implied Volatility in Eurodollar Options: A New Approach," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 10(2), pages 333-67.
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