Tell-Tale Tails: A data driven approach to estimate unique market information shares

The trading of securities on multiple markets raises the question of each market’s sharein the discovery of the informationally efficient price. We exploit salient distributionalfeatures of multivariate financial price processes to uniquely determine these contributions.Thereby we resolve the main drawback of the widely used Hasbrouck (1995)methodology which merely delivers upper and lower bounds of a market’s informationshare. When these bounds diverge, as is the case in many applications, informationalleadership becomes blurred. We show how fat tails and tail dependence of pricechanges, which emerge as a result of differences in market design and liquidity, can beexploited to estimate unique information shares. The empirical application of the newmethodology emphasizes the leading role of the credit derivatives market compared tothe corporate bond market in pricing credit risk during the pre-crisis period.