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Risk premia in commodity price forecasts and their impact on valuation

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  • Hahn, Warren J.
  • DiLellio, James A.
  • Dyer, James S.

Abstract

Commodity price driven valuation models require a stochastic price input if the value of managerial flexibility, such as the option to defer investment until the optimal time and the option to abandon a project, is to be estimated. The risk-neutral version of the stochastic price model is typically used in academic work; however, risk-adjusted models of the expected spot price are often used in practice. These two approaches are connected by a risk premium which is unfortunately often difficult to estimate. In this work, we use natural gas futures prices in a Kalman filter approach with maximum likelihood estimation to parameterize the Schwartz and Smith (2000) stochastic price model, and then apply an asset pricing model to address the large uncertainty of the risk premia parameter estimates. To evaluate the impact of the risk premia and other parameters in the two-factor price model on project valuation, we apply the price model to a prototypical shale gas investment, both for a base reference case as well as for cases where there are real options to optimally time decisions to invest or to abandon the project. Using this approach, we are able to determine the implied risk-adjusted discount rate that would be used with the spot price forecast, given the two-factor model risk premia, and we also discuss the impact of the risk premia on project value relative to other model parameters.

Suggested Citation

  • Hahn, Warren J. & DiLellio, James A. & Dyer, James S., 2018. "Risk premia in commodity price forecasts and their impact on valuation," Energy Economics, Elsevier, vol. 72(C), pages 393-403.
    • Handle: RePEc:eee:eneeco:v:72:y:2018:i:c:p:393-403
    DOI: 10.1016/j.eneco.2018.04.018
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Ladokhin, Sergiy & Borovkova, Svetlana, 2021. "Three-factor commodity forward curve model and its joint P and Q dynamics," Energy Economics, Elsevier, vol. 101(C).
    4. Babak Jafarizadeh, 2022. "Forecasts of Prices and Informed Sensitivity Analysis: Applications in Project Valuations," Decision Analysis, INFORMS, vol. 19(3), pages 205-219, September.
    5. Francisco J. Díaz-Borrego & María del Mar Miras-Rodríguez & Bernabé Escobar-Pérez, 2019. "Looking for Accurate Forecasting of Copper TC/RC Benchmark Levels," Complexity, Hindawi, vol. 2019, pages 1-16, April.
    6. Babak Jafarizadeh & Reidar B. Bratvold, 2021. "Project Valuation: Price Forecasts Bound to Discount Rates," Decision Analysis, INFORMS, vol. 18(2), pages 139-152, June.
    7. Jonek-Kowalska, Izabela, 2019. "Efficiency of Enterprise Risk Management (ERM) systems. Comparative analysis in the fuel sector and energy sector on the basis of Central-European companies listed on the Warsaw Stock Exchange," Resources Policy, Elsevier, vol. 62(C), pages 405-415.

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    More about this item

    Keywords

    Natural gas prices; Stochastic process; Kalman filter; Risk premia; Valuation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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