Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets
Roberto Casarin (),
Monica Billio () and
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Anthony Osuntuyi: Department of Mathematics, Obafemi Awolowo University
No 2014:07, Working Papers from Department of Economics, University of Venice "Ca' Foscari"
A new Bayesian multi-chain Markov Switching GARCH model for dynamic hedging in energy futures markets is developed by constructing a system of simultaneous equations for the return dynamics on the hedged portfolio and futures. 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. Moreover, the combination of the expected utility framework with regime-switching models allows the definition of a robust minimum variance hedging strategy to also account for parameter uncertainty. 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.
Keywords: Energy futures; GARCH; Hedge ratio; Markov-switching. (search for similar items in EconPapers)
JEL-codes: C1 C11 C15 C32 F31 G15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ore, nep-rmg and nep-upt
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Journal Article: Markov switching GARCH models for Bayesian hedging on energy futures markets (2018)
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