Seminar at SMU Delhi
January 12, 2012 (Thursday) ,
3:30 PM at Webinar
Seeking Hidden Risks with Multivariate Regular Variation
Abstract of Talk
Multivariate regular variation plays a role in assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to inaccurate and useless estimates of probabilities of joint tail regions. This problem can be partly ameliorated by using hidden regular variation. We offer a more flexible definition of hidden regular variation using a modified notion of convergence of measures that provides improved risk estimates for a larger class of tail risk regions. The new definition unifies ideas of asymptotic independence and asymptotic full dependence and avoids some deficiencies observed while using vague convergence. We also provide estimators for the limit measures.
(Joint work with A. Mitra and S. Resnick)