Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

By Jun Yu, Renate Meyer. 2006

207 citations recorded by the source corpus.

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/056c364b7c05c681054cf47d638ddeefa79925cc

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Citations in the corpus

The structure of dynamic correlations in multivariate stochastic volatility models (2009, methodology): “Recently, Yu and Meyer (2006) developed the time-varying correlation model for the bivariate SV model, based on the Fisher transformation, as suggested by Tsay (2002) in a bivariate GARCH framework.”
Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility (2018, unknown): “An alternative DIC can then be defined in terms of the conditional likelihood, which has been used in numerous applications (e.g., Abanto-Valle, Bandyopadhyay, Lachos, & Enriquez, 2010; Brooks & Prokopczuk, 2013; Mumtaz & Surico, 2012; Yu & Meyer, 2006).”
Multivariate Stochastic Volatility: A Review (2006, methodology): “Yu and Meyer (2006) showed that additive factor models accommodate both time-varying volatility and time-varying correlations.”
Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis (2011, methodology): “…', ) ( ', ) ( | ) ( | ') ( ') T T t t t t t p V Y p Y V p V p Y V p V p − − = = Θ ∝ Θ Θ ∝ Θ∏ ∏ Θ The software package WinBUGS (Bayesian inference using Gibbs Sampling) is employed for the computation of the bivariate SV model (see Meyer and Yu 2004 and Yu and Meyer 2008 for implementation details).”
An Analytical Formula for VIX Futures and its Applications (2012, methodology): “…software package WinBUGS, which provides an easy and efficient implementation of the Gibbs sampler, and has been successfully applied for a variety of statistic models such as random effects, generalized linear, proportional hazards, latent variables, and even state space models (Yu & Meyer 2006).”
Tails of correlation mixtures of elliptical copulas (2009, background): “This situation arises for instance in certain multivariate stochastic volatility models [7] and in the Stochastic Correlation Auto-Regressive (SCAR) model of [4].”
Multivariate stochastic volatility (2009, methodology): “Moreover, Yu and Meyer (2004) employed the purpose-built Bayesian software package called BUGS (Bayesian Analysis Using the Gibbs Sampler).”