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Simulation Monte Carlo methods in extended stochastic volatility models

Author

Listed:
  • Miroslav Šimandl
  • Tomáš Soukup

Abstract

A new technique for nonlinear state and parameter estimation of discrete time stochastic volatility models is developed. Algorithms of Gibbs sampler and simulation filters are used to construct a simulation tool that reflects both inherent model variability and parameter uncertainty. The proposed chain converges to equilibrium enabling the estimation of unobserved volatilities and unknown model parameter distributions. The estimation algorithm is illustrated using numerical examples. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Miroslav Šimandl & Tomáš Soukup, 2002. "Simulation Monte Carlo methods in extended stochastic volatility models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 11(2), pages 109-117, April.
    • Handle: RePEc:wly:isacfm:v:11:y:2002:i:2:p:109-117
    DOI: 10.1002/isaf.215
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    References listed on IDEAS

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    1. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
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    3. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
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    6. Watanabe, Toshiaki, 1999. "A Non-linear Filtering Approach to Stochastic Volatility Models with an Application to Daily Stock Returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 101-121, March-Apr.
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