Saunders, DavidTsui, Lung KwanIyengar, Satish2023-11-072023-11-072018https://doi.org/10.1017/s0269964817000353http://hdl.handle.net/10012/20089This article has been published in a revised form in Probability in the Engineering and Informational Sciences https://doi.org/10.1017/S0269964817000353. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University Press 2017.The coefficient of tail dependence is a quantity that measures how extreme events in one component of a bivariate distribution depend on extreme events in the other component. It is well known that the Gaussian copula has zero tail dependence, a shortcoming for its application in credit risk modeling and quantitative risk management in general. We show that this property is shared by the joint distributions of hitting times of bivariate (uniformly elliptic) diffusion processes.enhitting timeslarge deviationsSchilder's theoremsmall timeArticle