Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

TL;DR

This paper empirically evaluates the stochastic volatility model using hybrid Monte Carlo sampling on stock data, demonstrating its superior accuracy over GARCH models in volatility estimation.

Contribution

It provides an empirical comparison of the stochastic volatility model with GARCH, highlighting the effectiveness of HMC in Bayesian inference of volatility.

Findings

SV model outperforms GARCH in volatility accuracy

HMC algorithm effectively samples latent volatility variables

Empirical results based on Tokyo Stock Exchange data

Abstract

The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.