Capturing Tail Risks Beyond VaR

Since Value-at-Risk (VaR) disregards tail losses beyond the VaR boundary, the expected shortfall (ES), which measures the average loss when a VaR is exceeded, and the tail-risk-of-VaR (TR), which sums the sizes of tail losses, are used to investigate risks at the tails of distributions for major sto...

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Bibliographic Details
Published in:Review of Pacific basin financial markets and policies 2012-09, Vol.15 (3), p.1
Main Authors: Wong, Woon Kong, Fan, Guobin, Zeng, Yong
Format: Article
Language:English
Subjects:
Asymptotic methods
Risk assessment
Securities markets
Studies
Online Access:Get full text
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Summary:Since Value-at-Risk (VaR) disregards tail losses beyond the VaR boundary, the expected shortfall (ES), which measures the average loss when a VaR is exceeded, and the tail-risk-of-VaR (TR), which sums the sizes of tail losses, are used to investigate risks at the tails of distributions for major stock markets. As VaR exceptions are rare, we employ the saddlepoint or small sample asymptotic technique to backtest ES and TR. Because the two risk measures are complementary to each other and hence provide more powerful backtests, we are able to show that (a) the correct specification of distribution tail, rather than heteroscedastic process, plays a key role to accurate risk forecasts; and (b) it is best to model the tails separately from the central part of distribution using the Generalized Pareto Distribution (GPD). To sum up, we provide empirical evidence that financial markets behave differently during crises, and extreme risks cannot be modeled effectively under normal market conditions or based on a short data history. [PUBLICATION ABSTRACT]
ISSN:0219-0915
1793-6705