Abstract
The world crude oil prices have dropped dramatically, and consequently the oil market has become very volatile and risky in the last several years. Since energy markets play very important roles in the international economy and have led several global economic crises, risk management of energy products prices becomes very important for both academicians and market participants. Schwartz and Smith’s model (2000) is applied to calculate risk measures of Brent oil futures contracts and light sweet crude oil (WTI) futures contracts. The model includes a long-term factor and a short-term factor. We show that the two factors explain the Samuelson effect well and the model present well goodness of fit. Our back testing results demonstrate that the models provide satisfactory risk measures for listed crude oil futures contracts.
Highlights
The world crude oil prices have dropped dramatically, and the oil market has become very volatile and risky in the last several years
Risk management of energy products prices becomes very important for both academicians and market participants, and many risk measurement tools have been proposed in the literature
We show that Schwartz and Smith’s model could provide satisfactory risk measures for Brent crude oil futures and light sweet crude oil futures
Summary
The world crude oil prices have dropped dramatically, and the oil market has become very volatile and risky in the last several years. A non-exhausted list includes: Cabedo and Moya (2003), Costello, Asem and Gardner (2008), Krehbiel and Adkins (2005), Marimoutou, Raggad and Trabelsi (2009), Kang and Yoon (2013), Youssef, Belkacem, and Mokni (2015), and Fiano and Grossi (2015) These papers employ a widely-used risk measure, Value-at-Risk (VaR) originally proposed by J.P. Morgan in 1994 (see Duffie and Pan, 1997, for a discussion of this measure), but differ in the model assumptions. Most of the papers employ the stochastic multi-factor models for explaining futures prices fluctuations, and not directly from a risk management perspective. We illustrate that by employing the Monte Carlo methods the stochastic multi-factor models are powerful in calculating risk measures, such as Value at Risk or expected shortfall.
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