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Filtering and forecasting commodity futures prices under an HMM framework

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  • Date, Paresh
  • Mamon, Rogemar
  • Tenyakov, Anton

Abstract

We propose a model for the evolution of arbitrage-free futures prices under a regime-switching framework. The estimation of model parameters is carried out using the hidden Markov filtering algorithms. Comprehensive numerical experiments on real financial market data are provided to illustrate the effectiveness of our algorithm. In particular, the model is calibrated with data from heating oil futures and its forecasting performance as well as statistical validity is investigated. The proposed model is parsimonious, self-calibrating and can be very useful in predicting futures prices.

Suggested Citation

  • Date, Paresh & Mamon, Rogemar & Tenyakov, Anton, 2013. "Filtering and forecasting commodity futures prices under an HMM framework," Energy Economics, Elsevier, vol. 40(C), pages 1001-1013.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:1001-1013
    DOI: 10.1016/j.eneco.2013.05.016
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    References listed on IDEAS

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    1. Robert J. Elliott & Leunglung Chan & Tak Kuen Siu, 2005. "Option pricing and Esscher transform under regime switching," Annals of Finance, Springer, vol. 1(4), pages 423-432, October.
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    5. Erlwein, Christina & Benth, Fred Espen & Mamon, Rogemar, 2010. "HMM filtering and parameter estimation of an electricity spot price model," Energy Economics, Elsevier, vol. 32(5), pages 1034-1043, September.
    6. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
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    8. Robert J. Elliott & William C. Hunter & Barbara M. Jamieson, 2001. "Financial Signal Processing: A Self Calibrating Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 567-584.
    9. Katsushi Nakajima & Kazuhiko Ohashi, 2012. "A cointegrated commodity pricing model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(11), pages 995-1033, November.
    10. Zhuliang Chen & Peter Forsyth, 2010. "Implications of a regime-switching model on natural gas storage valuation and optimal operation," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 159-176.
    11. Hanfeng Chen & Jiahua Chen & John D. Kalbfleisch, 2001. "A modified likelihood ratio test for homogeneity in finite mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 19-29.
    12. Elliott, Robert J. & Hyndman, Cody. B., 2007. "Parameter estimation in commodity markets: A filtering approach," Journal of Economic Dynamics and Control, Elsevier, vol. 31(7), pages 2350-2373, July.
    13. Robert Elliott & Tak Siu & Leunglung Chan, 2008. "A PDE approach for risk measures for derivatives with regime switching," Annals of Finance, Springer, vol. 4(1), pages 55-74, January.
    14. Mihaela Manoliu & Stathis Tompaidis, 2002. "Energy futures prices: term structure models with Kalman filter estimation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 21-43.
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    Citations

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    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Chen, Yiyang & Mamon, Rogemar & Spagnolo, Fabio & Spagnolo, Nicola, 2022. "Renewable energy and economic growth: A Markov-switching approach," Energy, Elsevier, vol. 244(PB).
    3. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    4. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Insley, Margaret, 2017. "Resource extraction with a carbon tax and regime switching prices: Exercising your options," Energy Economics, Elsevier, vol. 67(C), pages 1-16.
    6. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    7. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
    8. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
    9. Islyaev, Suren & Date, Paresh, 2015. "Electricity futures price models: Calibration and forecasting," European Journal of Operational Research, Elsevier, vol. 247(1), pages 144-154.
    10. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    11. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.

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    More about this item

    Keywords

    Markov chain; Change of measure; Multivariate HMM filtering; Oil future prices;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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