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

TL;DR

This paper demonstrates that the hybrid Monte Carlo algorithm with a second-order integrator efficiently estimates parameters in the realized stochastic volatility model, showing reduced autocorrelation and improved performance.

Contribution

It introduces the use of the 2nd order minimum norm integrator within HMCA for RSV model estimation, enhancing efficiency over traditional methods.

Findings

2MNI outperforms leapfrog integrator in efficiency

HMCA yields very short autocorrelation times

Proves effective for Bayesian RSV parameter estimation

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.