2014/07/07 by Abhik Ghosh, Ghosh, Abhik
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Advanced Statistical Methods and Models #Applications (stat.AP) #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1407.1778
openalex publication_date 2014/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of estimating the coefficient of bivariate tail dependence is considered here from the robustness point of view; it combines two apparently contradictory theories of robust statistics and extreme value statistics. The usual maximum likelihood based or the moment type estimators of tail dependence coefficient are highly sensitive to the presence of outlying observations in data. This paper proposes some alternative robust estimators obtained by minimizing the density power divergence with suitable model assumptions; their robustness properties are examined through the classical influence function analysis. The performance of the proposed estimators is illustrated through an extensive empirical study considering several important bivariate extreme value distributions.