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

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Takaishi, Tetsuya^1

Hiroshima University of Economics, Hiroshima 731-0192, Japan^1

关键词: Autocorrelation time;    Bayesian estimations;    Bayesian parameter estimation;    Hybrid Monte Carlo algorithm;    Minimum norm;    Molecular dynamics simulations;    Stochastic volatility;    Stochastic Volatility Model;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012092/pdf
DOI  :  10.1088/1742-6596/490/1/012092

来源: IOP

【 摘 要 】

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.