Estimation of heavy tail dependence based on copulas for the precipitation analysis

Consideration of tail dependence is a very important part of risk analysis in many applied sciences that is measured in order to estimate the risk of simultaneous extreme events. Usually the tail dependence coefficient is the measurement in question. Pearson correlation coefficient unfortunately is not a suitable measure for estimating dependencies between two quantities in the context of simultaneous occurrence of extreme events when these events are of interest for the researcher because it takes extreme events into account with the same weight as it takes "normal" events although dependence of extreme values may slightly differ. Present work emphasizes the importance of taking into account tail dependencies in bivariate statistical analysis using copulas. Due to increasing frequency of environmental cataclysms the issue of analyzing risks (e.g. economic losses) and their consequences comes to the fore. Moreover, researchers should take into consideration consequences of their joint occurrence. Three non-parametric estimators of tail dependence coefficients were compared in order to estimate correlation between daily cumulative rainfall totals recorded in central European part of Russia. The majority of existing estimators depends on threshold and thus there is a trade-off between variance and bias during the calculation of the best value for For balancing an algorithm is presented that is based on using moving average filter and then searching the "stable" part of tail dependence coefficient. Estimate of tail dependence coefficient is assumed to be equal to mean value on the "stable" part. © 2018 CEUR-WS. All Rights Reserved.