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Jumps and stochastic volatility in oil prices: Time series evidence

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  • Larsson, Karl
  • Nossman, Marcus

Abstract

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time series study of crude oil prices. We compare four different models and estimate them using the Markov Chain Monte Carlo method. The support for a stochastic volatility model including jumps in both prices and volatility is strong and the model clearly outperforms the others in terms of a superior fit to data. Our estimation method allows us to obtain a detailed study of oil prices during two periods of extreme market stress included in our sample; the Gulf war and the recent financial crisis. We also address the economic significance of model choice in two option pricing applications. The implied volatilities generated by the different estimated models are compared and we price a real option to develop an oil field. Our findings indicate that model choice can have a material effect on the option values.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 33 (2011)
Issue (Month): 3 (May)
Pages: 504-514

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Handle: RePEc:eee:eneeco:v:33:y:2011:i:3:p:504-514

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Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: Oil prices Stochastic volatility Jump diffusion Markov Chain Monte Carlo Investment;

References

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  1. Mark Broadie & Mikhail Chernov & Michael Johannes, 2007. "Model Specification and Risk Premia: Evidence from Futures Options," Journal of Finance, American Finance Association, vol. 62(3), pages 1453-1490, 06.
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  8. Darrell Duffie & Jun Pan & Kenneth Singleton, 1999. "Transform Analysis and Asset Pricing for Affine Jump-Diffusions," NBER Working Papers 7105, National Bureau of Economic Research, Inc.
  9. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
  10. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
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Cited by:
  1. Chevallier, Julien & Aboura, Sofiane, 2013. "Leverage vs. Feedback: Which Effect Drives the Oil Market ?," Economics Papers from University Paris Dauphine 123456789/9860, Paris Dauphine University.
  2. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
  3. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 577-598, March.
  4. Aggarwal, Raj & Akhigbe, Aigbe & Mohanty, Sunil K., 2012. "Oil price shocks and transportation firm asset prices," Energy Economics, Elsevier, vol. 34(5), pages 1370-1379.
  5. Margaret Insley, 2013. "On the timing of non-renewable resource extraction with regime switching prices: an optimal stochastic control approach," Working Papers 1302, University of Waterloo, Department of Economics, revised Aug 2013.

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