Risk premia in commodity price forecasts and their impact on valuation

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

Energy Economics, 2018, vol. 72, issue C, 393-403

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.

Keywords: Natural gas prices; Stochastic process; Kalman filter; Risk premia; Valuation (search for similar items in EconPapers)
JEL-codes: C52 C53 Q47 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:72:y:2018:i:c:p:393-403

DOI: 10.1016/j.eneco.2018.04.018

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