2017/08/23 by Anna Kiriliouk, Kiriliouk, Anna · 1 citation
Economics, Econometrics and Finance · #62G32 #62H15 #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #Methodology (stat.ME) #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.1708.07019
openalex publication_date 2017/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modelling multivariate tail dependence is one of the key challenges in\nextreme-value theory. Multivariate extremes are usually characterized using\nparametric models, some of which have simpler submodels at the boundary of\ntheir parameter space. Hypothesis tests are proposed for tail dependence\nparameters that, under the null hypothesis, are on the boundary of the\nalternative hypothesis. The asymptotic distribution of the weighted least\nsquares estimator (Einmahl, Kiriliouk and Segers, Extremes 21, pages 205-233,\n2018) is given when the true parameter vector is on the boundary of the\nparameter space, and two test statistics are proposed. The performance of these\ntest statistics is evaluated for the Brown-Resnick model and the max-linear\nmodel. In particular, simulations show that it is possible to recover the\noptimal number of factors for a max-linear model. Finally, the methods are\napplied to characterize the dependence structure of two major stock market\nindices, the DAX and the CAC40.\n