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Extreme Value Approach (extreme + value_approach)
Selected AbstractsA Generalized Extreme Value Approach to Financial Risk MeasurementJOURNAL OF MONEY, CREDIT AND BANKING, Issue 7 2007TURAN G. BALI financial risk management; value at risk; extreme value theory; skewed fat-tailed distributions This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box,Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions. [source] Risk of catastrophic terrorism: an extreme value approachJOURNAL OF APPLIED ECONOMETRICS, Issue 4 2009Hamid Mohtadi This paper models the stochastic behavior of large-scale terrorism using extreme value methods. We utilize a unique dataset composed of roughly 26,000 observations. These data provide a rich description of domestic and international terrorism between 1968 and 2006. Currently, a credible worst-case scenario would involve losses of about 5000 to 10,000 lives. Also, the return time for events of such magnitude is shortening every year. Today, the primary threat is from conventional weapons, rather than from chemical, biological and/or radionuclear weapons. However, pronounced tails in the distribution of these incidents suggest that this threat cannot be dismissed. Copyright © 2009 John Wiley & Sons, Ltd. [source] A Generalized Extreme Value Approach to Financial Risk MeasurementJOURNAL OF MONEY, CREDIT AND BANKING, Issue 7 2007TURAN G. BALI financial risk management; value at risk; extreme value theory; skewed fat-tailed distributions This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box,Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions. [source] A new class of models for bivariate joint tailsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2009Alexandra Ramos Summary., A fundamental issue in applied multivariate extreme value analysis is modelling dependence within joint tail regions. The primary focus of this work is to extend the classical pseudopolar treatment of multivariate extremes to develop an asymptotically motivated representation of extremal dependence that also encompasses asymptotic independence. Starting with the usual mild bivariate regular variation assumptions that underpin the coefficient of tail dependence as a measure of extremal dependence, our main result is a characterization of the limiting structure of the joint survivor function in terms of an essentially arbitrary non-negative measure that must satisfy some mild constraints. We then construct parametric models from this new class and study in detail one example that accommodates asymptotic dependence, asymptotic independence and asymmetry within a straightforward parsimonious parameterization. We provide a fast simulation algorithm for this example and detail likelihood-based inference including tests for asymptotic dependence and symmetry which are useful for submodel selection. We illustrate this model by application to both simulated and real data. In contrast with the classical multivariate extreme value approach, which concentrates on the limiting distribution of normalized componentwise maxima, our framework focuses directly on the structure of the limiting joint survivor function and provides significant extensions of both the theoretical and the practical tools that are available for joint tail modelling. [source] | ||||||||||