Heavy-tailed Distributions and Risk Management of Equity Market Tail Events

Zi-Yi Guo

Journal of Risk & Control, 2017, vol. 4, issue 1, 31-41

Abstract: Traditional econometric modelling typically follows the idea that market returns follow a normal distribution. However, the concept of tail risk indicates that the distribution of returns is not normal, but skewed and has heavy tails. Thus, a heavy-tailed distribution, which accurately estimates the tail risk, would significantly improve quantitative risk management practice. In this paper, we compare four widely used heavy-tailed distributions using the S&P 500 daily returns. Our results indicate that the Skewed t distribution in Hansen (1994) has the superior empirical performance compared with the Student’s t distribution, the normal reciprocal inverse Gaussian distribution and the generalized hyperbolic distribution. We further showed the Skewed t distribution could generate the VaR estimates closest to the nonparametric historical VaR estimates compared with other heavy-tailed distributions.

Keywords: Tail risk; Value at Risk; Goodness of fit. (search for similar items in EconPapers)
JEL-codes: C46 C58 G10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:rmk:rmkjrc:v:4:y:2017:i:1:p:31-41

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