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Novel techniques for Bayesian inference in univariate and multivariate stochastic volatility models

Author

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  • 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.

Suggested Citation

  • Mike G. Tsionas, 2022. "Novel techniques for Bayesian inference in univariate and multivariate stochastic volatility models," Working Papers 294, Bank of Greece.
    • Handle: RePEc:bog:wpaper:294
    DOI: 10.52903/wp2021294
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    More about this item

    Keywords

    Stochastic volatility; response surface; likelihood; Monte Carlo.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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