Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...

In this paper we compare overall as well as downside risk measures with respect to the criteria of first and second order stochastic dominance. While the downside risk measures, with the exception of tail conditional expectation, are consistent with first order stochastic dominance, overall risk...

Accurate prediction of extreme events are of primary importance in many financial applications. The properties of historical simulation and Risk Metrics techniques for computing Valu-at Risk (VaR) are compared with a method which involves modelling the tails of financial returns explicitly with...

The various tools for risk measurement and management, especially for value-at-risk (VaR), are compared, with special emphasis on Japanese market data. Traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH-type methods are compared to extreme value theory (EVT). The...

Risk is endogenous. Equilibrium risk is the fixed point of the mapping that takes perceived risk to actual risk. When risk-neutral traders operate under Value-at-Risk constraints, market conditions exhibit signs of fluctuating risk appetite and amplification of shocks through feedback effects....

Large data sets in finance with millions of observations have become widely available. Such data sets enable the construction of reliable semi-parametric estimates of the risk associated with extreme price movements. Our approach is based on semi-parametric statistical extreme value analysis,...

This article contains comments on 'Bayesian Analysis of Stochastic Volatility Models,' by Jacquier, Polson, and Rossi. The Markov-chain Monte Carlo (MCMC) method proposed is compared empirically with a simulated maximum likelihood (SML) method. The MCMC and SML estimators yield very similar...