To this point, we have dealt almost exclusively with problems of estimation and statistical inference about a parameter of a probability distribution or characteristic of a sample. Another important element of applied statistical modeling of energy risks concerns the relationship between two or more price or other variables. Generally, a risk manager will be interested in whether above (below) average values of one variable tend to be associated with above (below) average values of the other variable. Take for example, a risk manger working for a petroleum refinery, who for hedging purposes, is interested in knowing the relationship between the spot price of Brent Crude and the future price of diesel fuel. If the risk manager simply assumes crude oil and diesel fuel prices always move in tandem, the company will be exposed to the price risk if this relationship breaks down. If on the other hand, the closeness of the two indices is defined in terms of a correlation coefficient, then the manager at least has some rudimentary way of assessing whether or not the relationship exists and its strength.
KeywordsEurope Covariance Diesel Gasoline Autocorrelation
Unable to display preview. Download preview PDF.
- Albers, W. (1999) “Stop-loss premiums under dependence,” Insurance: Mathematics and Economics, 24, 173–85.Google Scholar
- Bullimore J. (2000) “Jet Fuel Price Risk Management,” Swiss Derivatives Review, Autumn, 16, 20.Google Scholar
- Co, T. (2000) “High Prices Fuel Airlines’ Anxiety,” Asia Risk, November, Special 5th Anniversary Issue, 6.Google Scholar
- Galambos, J. (1987) The Asymptotic Theory of Extreme Order Statistics, Kreiger Publishing Co., Melbourne, FL.Google Scholar
- Horsewood, R. (1997), R. (1997) “Options and Oils,” Asia Risk, August 1997.Google Scholar
- Lewis, Nigel Da Costa (2004) Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, John Wiley & Sons, Inc., New York.Google Scholar