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Unexpected tails in risk measurement: Some international evidence

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  • Tolikas, Konstantinos

Abstract

Risk management critically depends on the assumptions made about the distribution of stock returns. This paper applies extreme value methods to investigate the limiting distribution of the extreme returns of the NIKKEI225, FTSE100 and S&P500 indices as well as the indices of some of largest sectors in Japan, UK and US. The results indicate that the much celebrated Generalised Extreme Value distribution does not provide the most accurate description of the minima since the Generalised Logistic distribution performs better due to its ability to better capture the fat tails of returns. The time varying nature of extremes is also confirmed while a simulation exercise adds to the robustness of our results. It is also shown that the findings may have important implications for risk models, such as VaR and Expected Shortfall, since risk measures which cannot capture the fatness of tails of the empirical distribution function of returns may lead to serious underestimation of downside risk.

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  • Tolikas, Konstantinos, 2014. "Unexpected tails in risk measurement: Some international evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 476-493.
    • Handle: RePEc:eee:jbfina:v:40:y:2014:i:c:p:476-493
    DOI: 10.1016/j.jbankfin.2013.07.022
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    Cited by:

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    2. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
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    4. Chikashi Tsuji, 2016. "Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220711-122, December.
    5. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    6. Joocheol Kim & HyunOh Kim, 2014. "Option Pricing with Generalized Logistic Distributions(published in:Global Economic Review, (2014) Vol.43, NO.3)," Working papers 2014rwp-66, Yonsei University, Yonsei Economics Research Institute.
    7. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
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    9. Lu Yang & Shigeyuki Hamori, 2020. "Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach," Energies, MDPI, vol. 13(14), pages 1-27, July.

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    More about this item

    Keywords

    Risk analysis; Extreme Value Theory; Value-at-Risk; Expected Shortfall; Fat tails;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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