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Extreme value theory as a theoretical background for power law behavior

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  • Alfarano, Simone
  • Lux, Thomas

Abstract

Power law behavior has been recognized to be a pervasive feature of many phenomena in natural and social sciences. While immense research efforts have been devoted to the analysis of behavioural mechanisms responsible for the ubiquity of power-law scaling, the strong theoretical foundation of power laws as a very general type of limiting behavior of large realizations of stochastic processes is less well known. In this paper, we briefly present some of the key results of extreme value theory, which provide a statistical justification for the emergence of power laws as limiting behavior for extreme fluctuations. The remarkable generality of the theory allows to abstract from the details of the system under investigation, and therefore allows its application in many diverse fields. Moreover, this theory offers new powerful techniques for the estimation of the Pareto index, detailed in the second part of this chapter.

Suggested Citation

  • Alfarano, Simone & Lux, Thomas, 2010. "Extreme value theory as a theoretical background for power law behavior," Kiel Working Papers 1648, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1648
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    References listed on IDEAS

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    1. Drees, Holger & Kaufmann, Edgar, 1998. "Selecting the optimal sample fraction in univariate extreme value estimation," Stochastic Processes and their Applications, Elsevier, vol. 75(2), pages 149-172, July.
    2. Phillip Kearns & Adrian Pagan, 1997. "Estimating The Density Tail Index For Financial Time Series," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 171-175, May.
    3. Hall, Peter, 1990. "Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 177-203, February.
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    Cited by:

    1. Noemi Schmitt & Frank Westerhoff, 2017. "Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1041-1070, November.
    2. Omar Blanco Arroyo & Simone Alfarano, 2017. "Granularity of the Business Cycle Fluctuations: The Spanish Case," Economia Coyuntural,Revista de temas de perspectivas y coyuntura, Instituto de Investigaciones Economicas y Sociales 'Jose Ortiz Mercado' (IIES-JOM), Facultad de Ciencias Economicas, Administrativas y Financieras, Universidad Autonoma Gabriel Rene Moreno, vol. 2(1), pages 31-58.
    3. Laurent Gauthier, 2022. "A Cliometrics and Complexity Perspective on Ancient Greek Culture," Working Papers hal-03315002, HAL.
    4. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

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    More about this item

    Keywords

    power law; estimation; tail index;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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