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A Generalized Schwartz Model for Energy Spot Prices - Estimation using a Particle MCMC Method

Author

Listed:
  • Asger Lunde

    () (Aarhus University and CREATES)

  • Anne Floor Brix

    () (Aarhus University and CREATES)

  • Wei Wei

    () (Aarhus University and CREATES)

Abstract

We propose an energy spot price model featuring a two-factor price process and a two-component stochastic volatility process. The first factor in the price process captures the normal variations; the second accounts for spikes. The two-component volatility allows for a flexible autocorrelation structure. Instead of using various filtering techniques for splitting the two factors, as often found in the literature, we estimate the model in one step using an adaptive MCMC method with a Rao-Blackwellized particle filter. We fit the model to UK natural gas spot prices and investigate the importance of spikes and stochastic volatility. We find that the inclusion of stochastic volatility is crucial and that it strongly impacts the jump intensity in the spike process. Furthermore, our estimation method enables us to consider both continuous and purely jump-driven volatility processes, and thereby assess if the volatility specification also influences the spike process and the overall model fit

Suggested Citation

  • Asger Lunde & Anne Floor Brix & Wei Wei, 2015. "A Generalized Schwartz Model for Energy Spot Prices - Estimation using a Particle MCMC Method," CREATES Research Papers 2015-46, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2015-46
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    File URL: ftp://ftp.econ.au.dk/creates/rp/15/rp15_46.pdf
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    References listed on IDEAS

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    Cited by:

    1. Fileccia, Gaetano & Sgarra, Carlo, 2018. "A particle filtering approach to oil futures price calibration and forecasting," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 21-34.

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    More about this item

    Keywords

    Energy prices; Multi-factor model; Particle filters; MCMC; Stochastic volatility;
    All these keywords.

    JEL classification:

    • 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
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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