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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...
Persistent link: https://www.econbiz.de/10004987421
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...
Persistent link: https://www.econbiz.de/10011256054
Persistent link: https://www.econbiz.de/10002081527
Under the new Capital Accord, banks choose between two different types of risk management systems, the standard or the internal rating based approach. The paper considers how a bank's preference for a risk management system is affected by the presence of supervision by bank regulators. The model...
Persistent link: https://www.econbiz.de/10010324867
We characterize the investor’s optimal portfolio allocation subject to a budget constraint and a probabilistic VaR constraint in complete markets environments with a finite number of states. The set of feasible portfolios might no longer be connected or convex, while the number of local optima...
Persistent link: https://www.econbiz.de/10010325054
Persistent link: https://www.econbiz.de/10010063355
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...
Persistent link: https://www.econbiz.de/10010324456
Worst-case analysis has increased in popularity among financial regulators in the wake of the recent financial crisis. In this paper we provide insight into this measure and provide some guidance on how to estimate it. We derive the bias for the non-parametric heavy tailed order-statistics and...
Persistent link: https://www.econbiz.de/10012961941
The selection of upper order statistics in tail estimation is notoriously difficult. Most methods are based on asymptotic arguments, like minimizing the asymptotic mse, that do not perform well in finite samples. Here we advance a data driven method that minimizes the maximum distance between...
Persistent link: https://www.econbiz.de/10013001136
Persistent link: https://www.econbiz.de/10012728634