Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage

TL;DR

This paper develops new Bayesian sampling algorithms for stochastic volatility models with leverage, improving efficiency and stability across different parameterizations, and compares them with existing methods using simulated and real data.

Contribution

It introduces novel algorithms for centered and non-centered parameterizations of SV models with leverage, combined via ASIS for stable efficiency.

Findings

Enhanced sampling efficiency across parameterizations

Stable Bayesian inference achieved with combined samplers

Demonstrated effectiveness on real-world financial data

Abstract

The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. We derive novel algorithms for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage, where the return process and innovations of the volatility process are allowed to correlate. Moreover, based on the idea of ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers in order to guarantee stable sampling efficiency irrespective of the baseline parameterization.We carry out an extensive comparison to already existing sampling methods for this model using simulated as well as real world data.