Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets

A new Bayesian multi-chain Markov Switching GARCHmodel for dynamic hedging in energy futures markets is developed: a system of simultaneous equations for return dynamics on the hedged portfolio and futures is introduced. More specifically, both the mean and variance of the hedged portfolio are assumed to be governed by two unobserved discrete state processes, while the futures dynamics is driven by a univariate hidden state process. The noise in both processes are characterized by a MS-GARCH model. This formulation has two main practical and conceptual advantages. First, the different states of the discrete processes can be identified as different volatility regimes. Secondly, the parameters can be easily interpreted as different hedging components. Our formulation also provides an avenue to analyze the contribution of the volatility dynamics and state probabilities to the optimal hedge ratio at each point in time. The application of expected utility framework combined with regime-switching models to define a robust minimum variance hedging strategy which accounts for parameter uncertainty is also presented. Evidence of changes in the optimal hedging strategies before and after the financial crisis is found when the proposed robust hedging strategy is applied to crude oil spot and futures markets.