Tell-tale tails: A data driven approach to estimate unique market information shares
AbstractThe 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. --
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Bibliographic InfoPaper provided by University of Cologne, Centre for Financial Research (CFR) in its series CFR Working Papers with number 10-06.
Date of creation: 2010
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price discovery; information share; fat tails; tail dependence; liquidity; credit risk;
Find related papers by JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-06 (All new papers)
- NEP-BAN-2010-11-06 (Banking)
- NEP-MST-2010-11-06 (Market Microstructure)
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