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Stock Market Volatility And The Forecasting Accuracy Of Implied Volatility Indices

Author

Listed:
  • 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)

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.

Suggested Citation

  • Kazuhiko NISHINA & Tatsuro Nabil MAGHREBI & Moo-Sung KIM, 2006. "Stock Market Volatility And The Forecasting Accuracy Of Implied Volatility Indices," Discussion Papers in Economics and Business 06-09, Osaka University, Graduate School of Economics.
    • Handle: RePEc:osk:wpaper:0609
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    References listed on IDEAS

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    9. Chambers, Donald R & Nawalkha, Sanjay K, 2001. "An Improved Approach to Computing Implied Volatility," The Financial Review, Eastern Finance Association, vol. 36(3), pages 89-99, August.
    10. Amin, Kaushik I & Ng, Victor K, 1997. "Inferring Future Volatility from the Information in Implied Volatility in Eurodollar Options: A New Approach," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 333-367.
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    Cited by:

    1. Costas Siriopoulos & Athanasios Fassas, 2013. "Dynamic relations of uncertainty expectations: a conditional assessment of implied volatility indices," Review of Derivatives Research, Springer, vol. 16(3), pages 233-266, October.
    2. Bekiros, Stelios & Jlassi, Mouna & Naoui, Kamel & Uddin, Gazi Salah, 2017. "The asymmetric relationship between returns and implied volatility: Evidence from global stock markets," Journal of Financial Stability, Elsevier, vol. 30(C), pages 156-174.
    3. Bollerslev, Tim & Marrone, James & Xu, Lai & Zhou, Hao, 2014. "Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 633-661, June.
    4. Hiroyuki Okawa, 2023. "Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics," JRFM, MDPI, vol. 16(2), pages 1-20, January.

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    More about this item

    Keywords

    Licensing; Implied volatility index; Out-of-sample forecasting; GARCH modelling;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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