Multivariate Tail Estimation: Conditioning on an extreme event

2015/02/25 by Rafał Kulik, Kulik, Rafał, Zhigang Tong +1
Economics, Econometrics and Finance · Mathematics · #Financial Risk and Volatility Modeling #Statistical Methods and Inference #Stochastic processes and financial applications #msc:62G32 #stat.ME

paper · pdf · doi:10.48550/arxiv.1502.07189

8 figures

arxiv created 2015/02/25 · arxiv updated 2015/02/26

Abstract

We consider regularly varying random vectors. Our goal is to estimate in a non-parametric way some characteristics related to conditioning on an extreme event, like the tail dependence coefficient. We introduce a quasi-spectral decomposition that allow to improve efficiency of estimators. Asymptotic normality of estimators is based on weak convergence of tail empirical processes. Theoretical results are supported by simulation studies.

Citations

• Statistics for Tail Processes of Markov Chains

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