Descriptive Statistics of Energy Prices and Returns

  • Nigel Da Costa Lewis
Part of the Finance and Capital Markets Series book series (FCMS)


Descriptive statistics are those statistical methods that are used to summarize the characteristics of a sample. The main purpose of descriptive statistics is to reduce the original sample into a handful of more understandable metrics without distorting or losing too much of the valuable information contained in the individual observations. We begin by collecting N observations {r1, r2,…,rN} on the price return random variable R. These measurements are then organized and summarized using techniques of descriptive statistics described in this chapter. In most cases we can compactly describe the characteristics of a sample using three distinctive classes of descriptive statistics. The first class summarizes the center of the distribution and are known as measures of central tendency. The second class summarizes the spread or dispersion of the sample and are commonly known as measures of dispersion. The third class known as shape statistics summarizes important elements of the shape of the underlying probability distribution implied by the sample. Our objective in using descriptive statistics is to describe as compactly as possible the key properties of empirical data. This information will then be used to assist us in selecting an appropriate probability model for modeling price risk.


Central Tendency Energy Price Market Risk Sample Standard Deviation Shape Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Further Reading

  1. Crow, E. L., Davis, F. A., and Maxfield, M. W. (1960) Statistics Manual, Dover Publications Inc., New York.Google Scholar
  2. Groeneveld, R. A. (1998) A class of quartile measures for kurtosis. American Statistician, 51, 325–329.Google Scholar
  3. Lewis, Nigel Da Costa (2003) Market Risk Modeling: Applied Statistical Methods for Practitioners. Risk Books, London.Google Scholar
  4. 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
  5. Moors, J. J. A. (1998) AQuantile alternative for kurtosis, Statistician, 37, 25–32.CrossRefGoogle Scholar
  6. Rowntree, D. (1981) Statistics Without Tears, Penguin Books, Middlesex, England.Google Scholar

Copyright information

© Nigel Da Costa Lewis 2005

Authors and Affiliations

  • Nigel Da Costa Lewis

There are no affiliations available

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