Value-at-risk and extreme returns
January 2000
Article
pp 239-270.
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We propose a semi-parametric method for unconditional Value-at-Risk (VaR) evaluation. The largest risks are modelled parametrically, while smaller risks are captured by the non-parametric empirical distribution function. A comparison of methods on a portfolio of stock and option returns reveals that at the 5 % level the RiskMetrics analysis is best, but for predictions of low probability worst outcomes, it strongly underpredicts the VaR while the semi-parametric method is the most accurate.
Keywords
Automatically Extracted Terms
- return
- method
- distribution
- sample
- portfolio
- value
- probability
- estimate
- estimation
- prediction
- model
- table
- result
- riskmetric
- simulation
- number
- garch
- value-at-risk
- estimator
- volatility
