DOI: https://doi.org/10.52903/wp2022294
NOVEL TECHNIQUES FOR BAYESIAN INFERENCE IN UNIVARIATE AND MULTIVARIATE STOCHASTIC VOLATILITY MODELS
Mike G. Tsionas
Lancaster University
Abstract
In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can be approximated accurately and efficiently using a response surface methodology. The approximation is across the plausible range of parameter values and all possible data and is found to be highly accurate. The methods extend easily to multivariate models and are applied to artificial data as well as ten exchange rates and all stocks of FTSE100 using daily data. Formal comparisons with multivariate GARCH models are undertaken using a special prior for the GARCH parameters. The comparisons are based on marginal likelihood and the Bayes factors.
Keywords: Stochastic volatility; response surface; likelihood; Monte Carlo.
JEL classifications: C13; C15
Acknowledgments: The author wishes to thank Dimitris Malliaropoulos and an anonymous reviewer for useful comments on an earlier version. The views expressed in this paper are those of the author and do not necessarily reflect those of the Bank of Greece. This research was conducted in the context of the Bank’s programme of cooperation with universities.
Correspondence:
Mike G. Tsionas
Department of Economics
Lancaster University Management School
Bailrigg, Lancaster, LA1 4YX,United Kingdom
Tel. telephone: +44 (0)1524 592668
Email: m.tsionas@lancaster.ac.uk