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Computational Examples of a New Method for Distribution Selection in the Pearson System

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  • Andriy Andreev
  • Antti Kanto
  • Pekka Malo

Abstract

A considerable problem in statistics and risk management is finding distributions that capture the complex behaviour exhibited by financial data. The importance of higher order moments in decision making has been well recognized and there is increasing interest in modelling with distributions that are able to account for these effects. The Pearson system can be used to model a wide scale of distributions with various skewness and kurtosis. This paper provides computational examples of a new easily implemented method for selecting probability density functions from the Pearson family of distributions. We apply this method to daily, monthly, and annual series using a range of data from commodity markets to macroeconomic variables.

Suggested Citation

  • Andriy Andreev & Antti Kanto & Pekka Malo, 2007. "Computational Examples of a New Method for Distribution Selection in the Pearson System," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 487-506.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:487-506
    DOI: 10.1080/02664760701231922
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    References listed on IDEAS

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    Cited by:

    1. Tonghui Wei & Wenjie Zuo & Hongwei Zheng & Feng Li, 2021. "Slope hybrid reliability analysis considering the uncertainty of probability-interval using three-parameter Weibull distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 565-586, January.
    2. Benjamin R. Auer, 2022. "On false discoveries of standard t-tests in investment management applications," Review of Managerial Science, Springer, vol. 16(3), pages 751-768, April.

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