Unexpected tails in risk measurement: Some international evidence

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)476-493
Number of pages17
JournalJournal of Banking and Finance
Volume40
DOIs
Publication statusPublished - 1 Mar 2014

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Risk measurement
Simulation
Generalized extreme value distribution
Logistics/distribution
Time-varying
Distribution function
Extreme returns
Empirical distribution
Fat tails
Risk management
Japan
Stock returns
Risk measures
Risk model
Extreme values
Limiting distribution
Exercise
Expected shortfall
Robustness
Downside risk

Cite this

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title = "Unexpected tails in risk measurement: Some international evidence",
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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Unexpected tails in risk measurement : Some international evidence. / Tolikas, Konstantinos.

In: Journal of Banking and Finance, Vol. 40, 01.03.2014, p. 476-493.

Research output: Contribution to journalArticle

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