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 share in the discovery of the informationally efficient price. We exploit salient distributional features 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 information share. When these bounds diverge, as is the case in many applications, informational leadership becomes blurred. We show how fat tails and tail dependence of price changes, which emerge as a result of differences in market design and liquidity, can be exploited to estimate unique information shares. The empirical application of the new methodology emphasizes the leading role of the credit derivatives market compared to the corporate bond market in pricing credit risk during the pre-crisis period.