  We apply the hybrid Monte Carlo (HMC) algorithm to the financial time sires
analysis of the stochastic volatility (SV) model for the first time. The HMC
algorithm is used for the Markov chain Monte Carlo (MCMC) update of volatility
variables of the SV model in the Bayesian inference. We compute parameters of
the SV model from the artificial financial data and compare the results from
the HMC algorithm with those from the Metropolis algorithm. We find that the
HMC decorrelates the volatility variables faster than the Metropolis algorithm.
We also make an empirical analysis based on the Yen/Dollar exchange rates.
