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Nonlinear Adjustments of Volatility Expectations to Forecast Errors: Evidence from Markov-Regime Switches in Implied Volatility

Author

Listed:
  • Kazuhiko Nishina

    (Faculty of Economics, Meiji Gakuin University, Japan;
    Center for the Study of Finance and Insurance, Osaka University, Japan)

  • Nabil Maghrebi

    (Graduate School of Economics, Wakayama University, Japan;
    Center for the Study of Finance and Insurance, Osaka University, Japan)

  • Mark J. Holmes

    (Department of Economics, Waikato University Management School, Private Bag 3105, Hamilton 3240, New Zealand)

Abstract

This paper tests for nonlinearities in the behavior of volatility expectations based on model-free implied volatility indices. Using Markov regime-switching models, the empirical evidence from the German, Japanese and U.S. markets suggests that there are indeed regime-specific levels of volatility expectations. Whereas the regimes seem to be governed by the degree of serial correlation and adjustment to forecast errors, there is no evidence of significant leverage effects. The frequency of regime shifts in volatility expectations is affected by the onset of financial crises, which have the effect of increasing the likelihood of regimes driven by lower autoregressive effects and faster speeds of adjustment. The evidence suggests that despite the heterogeneous beliefs of market participants, implied volatility indices provide a measure of consensus expectations that can be useful in understanding the nonlinear behavior of volatility expectations during periods of financial instability.

Suggested Citation

  • Kazuhiko Nishina & Nabil Maghrebi & Mark J. Holmes, 2012. "Nonlinear Adjustments of Volatility Expectations to Forecast Errors: Evidence from Markov-Regime Switches in Implied Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-23.
    • Handle: RePEc:wsi:rpbfmp:v:15:y:2012:i:03:n:s0219091512500075
    DOI: 10.1142/S0219091512500075
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    Citations

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    Cited by:

    1. David Newton, 2019. "Are All Forecasts Made Equal? Conditioning Models on Fit to Improve Accuracy," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-32, September.
    2. Mi-Hsiu Chiang & Chang-Yi Li & Son-Nan Chen, 2016. "Pricing currency options under double exponential jump diffusion in a Markov-modulated HJM economy," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 459-482, April.
    3. Massimo Costabile & Arturo Leccadito & Ivar Massabó & Emilio Russo, 2014. "A reduced lattice model for option pricing under regime-switching," Review of Quantitative Finance and Accounting, Springer, vol. 42(4), pages 667-690, May.
    4. Arturo Leccadito & Stefania Veltri, 2015. "A regime switching Ohlson model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2015-2035, September.
    5. Yao Zheng & Eric Osmer, 2018. "The Relationship between Hedge Fund Performance and Stock Market Sentiment," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 1-29, September.
    6. Nabil Maghrebi & Mark J. Holmes & Kosuke Oya, 2014. "Financial instability and the short-term dynamics of volatility expectations," Applied Financial Economics, Taylor & Francis Journals, vol. 24(6), pages 377-395, March.
    7. Miles Gietzmann & Adam Ostaszewski, 2014. "Why managers with low forecast precision select high disclosure intensity: an equilibrium analysis," Review of Quantitative Finance and Accounting, Springer, vol. 43(1), pages 121-153, July.

    More about this item

    Keywords

    Markov-Regime switching model; implied volatility index; nonlinear modeling;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance

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