  The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference
of the stochastic volatility (SV) model. We use the HMC algorithm for the
Markov chain Monte Carlo updates of volatility variables of the SV model. First
we compute parameters of the SV model by using the artificial financial data
and compare the results from the HMC algorithm with those from the Metropolis
algorithm. We find that the HMC algorithm decorrelates the volatility variables
faster than the Metropolis algorithm. Second we make an empirical study for the
time series of the Nikkei 225 stock index by the HMC algorithm. We find the
similar correlation behavior for the sampled data to the results from the
artificial financial data and obtain a $\phi$ value close to one ($\phi \approx
0.977$), which means that the time series has the strong persistency of the
volatility shock.
