Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction

Audrone Virbickaite, M. Concepción Ausín and Pedro Galeano

Energy Economics, 2020, vol. 92, issue C

Abstract: Modeling the volatility of energy commodity returns has become a topic of increased interest in recent years, because of the important role it plays in today's economy. In this paper we propose a novel copula-based stochastic volatility model for energy commodity returns that allows for asymmetric volatility persistence. We employ Approximate Bayesian Computation (ABC), a powerful tool to make inferences and predictions for such highly-nonlinear model. We carry out two simulation studies to illustrate that ABC is an appropriate alternative to standard MCMC-based methods when the state transition process is challenging to implement. Finally, we model the volatility of WTI and Brent oil futures' returns with the proposed copula-based stochastic volatility model and show that such model outperforms symmetric alternatives in terms of in- and out-of-sample volatility prediction accuracy.

Keywords: ABC; Bayesian inference; Energy commodity returns; MCMC; Realized volatility (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:92:y:2020:i:c:s0140988320303017

DOI: 10.1016/j.eneco.2020.104961

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