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Modelling and Estimating the Forward Price Curve in the Energy Market

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Abstract

The stochastic or random nature of commodity prices plays a central role in models for valuing financial contingent claims on commodities. In this paper, by enhancing a multifactor framework which is consistent not only with the market observable forward price curve but also the volatilities and correlations of forward prices, we propose a two factor stochastic volatility model for the evolution of the gas forward curve. The volatility is stochastic due to a hidden Markov Chain that causes it to switch between "on peak" and "off peak" states. Based on the structure functional forms for the volatility, we propose and implement the Markov Chain Monte Carlo (MCMC) method to estimate the parameters of the forward curve model. Applications to simulated data indicate that the proposed algorithm is able to accommodate more general features, such as regime switching and seasonality. Applications to the market gas forward data shows that the MCMC approach provides stable estimates.

Suggested Citation

  • Carl Chiarella & Les Clewlow & Boda Kang, 2009. "Modelling and Estimating the Forward Price Curve in the Energy Market," Research Paper Series 260, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:260
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    File URL: https://www.uts.edu.au/sites/default/files/qfr-archive-03/QFR-rp260.pdf
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    References listed on IDEAS

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    1. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    2. Benth, Fred Espen & Koekebakker, Steen, 2008. "Stochastic modeling of financial electricity contracts," Energy Economics, Elsevier, vol. 30(3), pages 1116-1157, May.
    3. Les Clewlow & Chris Strickland, 1999. "Valuing Energy Options in a One Factor Model Fitted to Forward Prices," Research Paper Series 10, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Gibson, Rajna & Schwartz, Eduardo S, 1990. "Stochastic Convenience Yield and the Pricing of Oil Contingent Claims," Journal of Finance, American Finance Association, vol. 45(3), pages 959-976, July.
    5. David Heath & Robert Jarrow & Andrew Morton, 2008. "Bond Pricing And The Term Structure Of Interest Rates: A New Methodology For Contingent Claims Valuation," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 13, pages 277-305, World Scientific Publishing Co. Pte. Ltd..
    6. Les Clewlow & Chris Strickland, 1999. "A Multi-Factor Model for Energy Derivatives," Research Paper Series 28, Quantitative Finance Research Centre, University of Technology, Sydney.
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    Cited by:

    1. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    2. Almansour, Abdullah, 2016. "Convenience yield in commodity price modeling: A regime switching approach," Energy Economics, Elsevier, vol. 53(C), pages 238-247.
    3. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    4. Carl Chiarella & Les Clewlow & Boda Kang, 2016. "The Evaluation Of Multiple Year Gas Sales Agreement With Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-25, February.

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