Multivariate Stochastic Volatility-Double Jump Model: An Application for Oil Assets

Abstract

We propose a new multivariate model to capture the presence of jumps in mean and conditional variance in the returns of oil prices and companies in this sector. The model is based on the presence of common factors associated with jumps in mean and variance, as it performs a decomposition of the conditional variance of each asset as the sum of the common factor plus a specific transitory factor in a multivariate stochastic volatility structure. The estimation is made through Bayesian methods using Markov Chain Monte Carlo. The model allows recovering the changes in prices and volatility patterns observed in this sector, relating the jumps with the events observed in the period 2000-2015. We apply the model to estimate risk management measures, hedging and portfolio allocation and performing a comparison with other multivariate models of conditional volatility. Based on the results, we may conclude that the proposed model has a better performance when used to calculate portfolio VaR, since it does not reject the hypothesis of correct nominal coverage with certain specifications presented in this work. Furthermore, we conclude that the model can be used to hedge oil price risks, through the optimal hedge ratio for a portfolio containing an oil company as-set (stock) and the oil price contract. When compared to the standard methodology based on GARCH models, our model performs well in this application.