Financial Time Series Analysis of SV Model by Hybrid Monte Carlo

TL;DR

This paper introduces the application of the hybrid Monte Carlo algorithm to Bayesian inference in stochastic volatility models for financial time series, demonstrating improved decorrelation of volatility variables.

Contribution

It is the first to apply HMC to SV model analysis in finance, showing faster decorrelation compared to traditional Metropolis methods.

Findings

HMC decorrelates volatility variables faster than Metropolis.

HMC provides accurate parameter estimation from artificial data.

Empirical analysis on Yen/Dollar exchange rates supports effectiveness.

Abstract

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.