Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/85738
Authors: 
Daníelsson, Jón
de Vries, Casper G.
Year of Publication: 
1998
Series/Report no.: 
Tinbergen Institute Discussion Paper 98-017/2
Abstract: 
Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the non-parametric empirical distribution function. The semi-parametric method is compared with historicalsimulation and the J.P. Morgan RiskMetrics technique on a portfolio of stock returns. For predictions oflow probability worst outcomes, RiskMetrics analysis underpredicts the VaR while historical simulationoverpredicts the VaR. However, the estimates obtained from applying the semi-parametric method aremore accurate in the VaR prediction. In addition, an option is used in the portfolio to lower downsiderisk. Finally, it is argued that current regulatory environment provides incentives to use the lowestquality VaR method available.
Subjects: 
Value-at-Risk
Extreme Value Theory
RiskMetrics
Historical Simulation
Tail Density Estimation
Financial Regulation
Document Type: 
Working Paper

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