Measuring financial contagion by local Gaussian correlation

This paper examines financial contagion, that is, whether the cross-market linkages in financial markets increases after a shock to a country. This effect has been studied in a variety of ways. We introduce the use of a new measure of local dependence to study the contagion effect. The central idea of the new approach is to approximate an arbitrary bivariate return distribution by a family of Gaussian bivariate distributions. At each point of the return distribution there is a Gaussian distribution that gives a good approximation at that point. The correlation of the approximating Gaussian distribution is taken as the local correlation in that neighbourhood. By comparing the local Gaussian correlation for the stable and crisis period, we are able to test whether contagion has occurred. In particular, there are several advantages by using the local Gaussian correlation; the new measure does not suffer from the selection bias of the conditional correlation for Gaussian data, and the local Gaussian correlation may be able to detect complex, nonlinear changes in the dependence structure, that a global correlation may mask. Examining several crisis, among others the Asian crisis of 1997 and the financial crisis of 2007-2008, we find evidence of contagion based on our new procedure.