Modeling Energy Price Volatility
Say the words “Energy price volatility” and many people will think of the OPEC oil price hikes of the 1970s or perhaps the more recent 2004 sharp upswing in the price of crude oil. Yet price volatility is a characteristic of capitalism and freely operating energy markets are no exception. Accurate estimates of the variation in energy asset values over time are important for the valuation of financial contracts, retail obligations, physical assets, and in solving portfolio allocation problems. As a consequence, modeling and forecasting of price volatility has acquired an unprecedented significance in the industry. In response, various attempts have been made to develop statistical tools to help characterize and predict price volatility. In general these models fall into three categories, Exponentially Weighted Moving Average models, Generalized Autoregressive Conditional Hetroscedasticity models, and Stochastic Volatility Differential Equations. In this chapter we introduce the first two of these modeling approaches.
KeywordsCovariance Gasoline Autocorrelation Volatility Guaran
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- Guermat, C. and Harris, R. D. F. (2001) “Robust conditional variance estimation and value-at-risk;” Discussion Papers in Accounting and Finance, 01/06, University of Exeter.Google Scholar
- More general reading on the theoretical development and various applications of GARCH models is given in:Google Scholar
- Andersen, T. G. (1994) “Stochastic autoregressive volatility: a framework for volatility modeling,” Mathematical Finance, 4, 75–102.Google Scholar
- Bollerslev, T., Engle, R. F., and Nelson, D. B. (1994) “ARCH Models,” in Handbook of Econometrics Vol. IV (R. F. Engle and D. McFadden, eds) North Holland Press, Amsterdam.Google Scholar
- Cumby, R., Figlewski, S., and Hasbrouck, J. (1993) “Forecasting volatility and correlations with EGARCH models,” Journal of Derivatives, Winter, 51–63.Google Scholar
- Diebold, F. X. and Mariano, R. S. (1995) “Comparing predictive accuracy,” Journal of Business and Economic Statistics, 13, 253–63.Google Scholar
- Frennberg, P. and Hansson, B. (1996) “An evaluation of alternative models for predicting stock volatility: evidence from a small stock market,” Journal of International Financial Markets, Institutions and Money, 5, 117–34.Google Scholar
- Heynen, R. C. and Kat, H. M. (1994) “Volatility prediction: a comparison of the stochastic volatility, GARCH(1,1), and EGARCH(1,1) models,” Journal of Derivatives, Winter, 50–65.Google Scholar
- Pagan, A. R. and Sabau, H. (1992) “Consistency tests for heteroskedasticity and risk models,” Estudios Económicos, 7, 3–30.Google Scholar