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A Generalized SSAR Model and Predictive Distribution with an Application to VaR

Author

Listed:
  • Naoto Kunitomo

    (Faculty of Economics, University of Tokyo)

  • Seisho Sato

    (Institute of Statistical Mathematics)

Abstract

The asymmetrical movements between the downward and upward phases of the sample paths of time series have been sometimes observed. By generalizing the SSAR (simultaneous switching autoregressive) models, we introduce a class of nonlinear time series models having the asymmetrical sample paths in the upward and downward phases. We show that the class of generalized SSAR models is useful for estimating the asymmetrical predictive distribution given the present and past information. Applications to the prediction based on the predictive median and the estimation of the VaR (value at risk) in financial risk management are discussed.

Suggested Citation

  • Naoto Kunitomo & Seisho Sato, 2001. "A Generalized SSAR Model and Predictive Distribution with an Application to VaR," CIRJE F-Series CIRJE-F-122, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2001cf122
    as

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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Seisho Sato & Naoto Kunitomo, 1996. "Some Properties Of The Maximum Likelihood Estimator In The Simultaneous Switching Autoregressive Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(3), pages 287-307, May.
    5. Naoto Kunitomo & Seisho Sato, 1999. "Stationary and Non-stationary Simultaneous Switching Autoregressive Models with an Application to Financial Time Series," The Japanese Economic Review, Japanese Economic Association, vol. 50(2), pages 161-190, June.
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