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Bayesian Analysis of the Box-Cox Transformation in Stochastic Volatility Models

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  • Anna Pajor

    (Cracow University of Economics)

Abstract

In the paper, we consider the Box-Cox transformation of financial time series in Stochastic Volatility models. Bayesian approach is applied to make inference about the Box-Cox transformation parameter (l). Using daily data (quotations of stock indices), we show that in the Stochastic Volatility models with fat tails and correlated errors (FCSV), the posterior distribution of parameter l strongly depends on the prior assumption about this parameter. In the majority of cases the values of l close to 0 are more probable a posteriori than the ones close to 1.

Suggested Citation

  • Anna Pajor, 2009. "Bayesian Analysis of the Box-Cox Transformation in Stochastic Volatility Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 81-90.
    • Handle: RePEc:cpn:umkdem:v:9:y:2009:p:81-90
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    References listed on IDEAS

    as
    1. Christian M. Hafner & Wolfgang HÄrdle, 2000. "Discrete time option pricing with flexible volatility estimation," Finance and Stochastics, Springer, vol. 4(2), pages 189-207.
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    3. Bauwens, Luc & Lubrano, Michel, 2002. "Bayesian option pricing using asymmetric GARCH models," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 321-342, August.
    4. 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.
    5. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
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