Series: Working Papers. 1227.
Author: Lorenzo Ricci and David Veredas.
Topics: Exchange rates | Crisis | Government debt | Quantitative methods | Business investment.
Full document
Abstract
We introduce TailCoR, a new measure for tail correlation that is a function of linear and non-linear correlations, the latter characterized by the tail index. TailCoR can be exploited in a number of financial applications, such as portfolio selection where the investor faces risks of a linear and tail nature. Moreover, it has the following advantages: i) it is exact for any probability level as it is not based on tail asymptotic arguments (contrary to tail dependence coefficients), ii) it can be used in all tail scenarios (fatter, equal to or thinner than those of the Gaussian distribution), iii), it is distribution free, and iv) it is simple and no optimizations are needed. Monte Carlo simulations and calibrations reveal its goodness in finite samples. An empirical illustration using a panel of Euro area sovereign bonds shows that prior to 2009 linear correlations were in the vicinity of one and non-linear correlations were inexistent. Since the beginning of the crisis the linear correlations have decreased sharply, and non-linear correlations appeared and increased signifi cantly in 2010-2011.