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Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets

Author

Listed:
  • Roberto Casarin

    (Department of Economics, University of Venice C� Foscari)

  • Monica Billio

    (Department of Economics, University of Venice, C� Foscari)

  • Anthony Osuntuyi

    (Department of Mathematics, Obafemi Awolowo University)

Abstract

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.

Suggested Citation

  • Roberto Casarin & Monica Billio & Anthony Osuntuyi, 2014. "Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets," Working Papers 2014:07, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2014:07
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    More about this item

    Keywords

    Energy futures; GARCH; Hedge ratio; Markov-switching.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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