Extreme value statistics (EVS) provides instruments to estimate rare events and appears to be ideal for estimating tail based risk measures such as Value at Risk (VaR) and Expected Shortfall (ES). This thesis provides a concise summary of the theory of univariate EVS with an emphasis on quantile estimation, and an empirical comparison of EVS based VaR and ES estimation with more traditional VaR/ES estimators. Requirements: programming skills, good understanding of statistical estimation, basic understanding of GARCH models, experience with financial data.

References:

1. MCNEIL, A. J. and FREY, R. (2000): “Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach”, Journal of Empirical Finance, 7, 271-300
2. LONGIN, F. M. (2000): “From value at risk to stress testing: The extreme value approach”, Journal of Banking & Finance, 24(7), 1097-1130
3. GILLI, M. and KELLEZI, E. (2006): “An application of extreme value theory for measuring financial risk“, Computational Economics, 27, 207-228