NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:2122
Title:Cross-sectional Learning of Extremal Dependence among Financial Assets

This paper proposes a new approach to model tail dependence between random variables, i.e. dependence in the case of extreme events, which is different from naive correlation coefficient. Paper develops a methodology for estimating such models and presents a financial application. Experimental results are also provided. Overall, the results can be of interest beyond financial community and believe this would be a good contribution to Neurips this year.