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Detecting structural breaks in tail behaviour -- from the perspective of fitting the generalized Pareto distribution

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  • Wei-han Liu

Abstract

Extreme Value Theory (EVT) is heavily applied in modelling tail behaviour. Previous literature uses the tail index to test for Structural Breaks (SBs) in the tails. This study presents another more reliable approach and relies on the outperformance of the Generalized Pareto Distribution (GPD) in modelling tails. The transformed GPD is treated as a classical Ordinary Least Square (OLS) regression and the generalized M-fluctuation test (Zeileis, 2005, 2006) is applied because it is a unified approach based on Maximum Likelihood (ML) scores (Andrews and Ploberger, 1994), F -statistics (1989, 1992), and OLS residuals (Ploberger and Kramer, 1992). The outcomes indicate that there are multiple SBs not only in all of the three exchange return series considered (UK Pound, Japanese Yen and New Taiwan Dollar, all versus US Dollar) but also GPD parameter estimation at extreme quantile levels. Based on these empirical analyses, it is advisable that EVT should be used with caution at extreme quantile levels.

Suggested Citation

  • Wei-han Liu, 2013. "Detecting structural breaks in tail behaviour -- from the perspective of fitting the generalized Pareto distribution," Applied Economics, Taylor & Francis Journals, vol. 45(10), pages 1273-1286, April.
    • Handle: RePEc:taf:applec:45:y:2013:i:10:p:1273-1286
    DOI: 10.1080/00036846.2011.613803
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    References listed on IDEAS

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    1. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, November.
    2. Yannick Malevergne & Didier Sornette, 2006. "Extreme Financial Risks : From Dependence to Risk Management," Post-Print hal-02298069, HAL.
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    Cited by:

    1. Liu, Wei-han, 2018. "Hidden Markov model analysis of extreme behaviors of foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1007-1019.

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