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Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm

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  • Tetsuya Takaishi

Abstract

The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model.

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  • Tetsuya Takaishi, 2014. "Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm," Papers 1408.0981, arXiv.org.
    • Handle: RePEc:arx:papers:1408.0981
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    Cited by:

    1. Takaishi, Tetsuya, 2018. "Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 139-154.

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