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tail behavior of a threshold autoregressive stochastic volatility model

Author

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  • Aliou Diop

    (IDHE - Institutions et Dynamiques Historiques de l'Economie - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Dominique Guegan

    (IDHE - Institutions et Dynamiques Historiques de l'Economie - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

We consider a threshold autoregressive stochastic volatility model where the driving noises are sequences of iid regurlarly random vatiables. We prove that both the right and the left tails of the marginal distribution of the log-volatility process are regularly varying with tail exponent. We also determine the exact values of the coefficients in the tail of the considered process.

Suggested Citation

  • Aliou Diop & Dominique Guegan, 2005. "tail behavior of a threshold autoregressive stochastic volatility model," Post-Print halshs-00188530, HAL.
    • Handle: RePEc:hal:journl:halshs-00188530
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00188530
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    References listed on IDEAS

    as
    1. Stephen J. Taylor, 1994. "Modeling Stochastic Volatility: A Review And Comparative Study," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 183-204, April.
    2. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. So, Mike K P & Li, W K & Lam, K, 2002. "A Threshold Stochastic Volatility Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 473-500, November.
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