Hostname: page-component-848d4c4894-pftt2 Total loading time: 0 Render date: 2024-05-23T20:47:01.228Z Has data issue: false hasContentIssue false

Telltale Tails: A New Approach to Estimating Unique Market Information Shares

Published online by Cambridge University Press:  03 May 2013

Joachim Grammig
Affiliation:
joachim.grammig@uni-tuebingen.de, Department of Economics, University of Tübingen, Mohlstrasse 36, D-72074 Tübingen, Germany, and Centre for Financial Research
Franziska J. Peter
Affiliation:
franziska-julia.peter@uni-tuebingen.de, Department of Economics, University of Tübingen, Mohlstrasse 36, D-72074 Tübingen, Germany.

Abstract

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 resolving the main drawback of the widely used Hasbrouck (1995) methodology, which merely provides upper and lower bounds of a market’s information share. We show how tail dependence of price changes, which may emerge as a result of differences in market design, can be exploited to estimate unique information shares. Two empiricalapplications illustrate the practical use of the new methodology.

Type
Research Articles
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baillie, R. T.; Geoffrey, B. G.; Tse, Y.; and Zabotina, T.. “Price Discovery and Common Factor Models.” Journal of Financial Markets, 5 (2002), 309321.CrossRefGoogle Scholar
Blanco, R.; Brennan, S.; and Marsh, I. W.. “An Empirical Analysis of the Dynamic Relation Between Investment-Grade Bonds and Credit Default Swaps.” Journal of Finance, 60 (2005), 22552281.CrossRefGoogle Scholar
Booth, G.; Lin, J.-C.; Martikainen, T.; and Tse, Y.. “Trading and Pricing in Upstairs and Downstairs Stock Markets.” Review of Financial Studies, 15 (2002), 11111135.CrossRefGoogle Scholar
Booth, G.; So, R.; and Tse, Y.. “Price Discovery in the German Equity Derivatives Markets.” Journal of Futures Markets, 19 (1999), 619643.3.0.CO;2-M>CrossRefGoogle Scholar
Collin-Dufresne, P.; Goldstein, R. S.; and Spencer, M.. “The Determinants of Credit Spread Changes.” Journal of Finance, 56 (2001), 21772207.CrossRefGoogle Scholar
Davidson, R., and MacKinnon, J. G.. Econometric Theory and Methods. Oxford: Oxford University Press (2000).Google Scholar
De Jong, F. “Measures of Contributions to Price Discovery: A Comparison.” Journal of Financial Markets, 5 (2002), 323327.CrossRefGoogle Scholar
Duffie, D. “Credit Swap Valuation.” Financial Analysts Journal, 55 (1999), 7387.CrossRefGoogle Scholar
Easley, D.; Kiefer, N.; O’Hara, M.; and Paperman, J. P.. “Liquidity, Information and Infrequently Traded Stocks.” Journal of Finance, 51 (1996), 14051436.CrossRefGoogle Scholar
Easley, D., and O’Hara, M.. “Time and the Process of Security Price Adjustment.” Journal of Finance, 47 (1992), 577605.CrossRefGoogle Scholar
Engle, R. F., and Granger, C.. “Cointegration and Error Correction: Representation, Estimation, and Testing.” Econometrica, 55 (1987), 9871007.CrossRefGoogle Scholar
Eun, C. S., and Sabherwal, S.. “Cross-Border Listings and Price Discovery: Evidence from U.S.-Listed Canadian Stocks.” Journal of Finance, 58 (2003), 549575.CrossRefGoogle Scholar
Foucault, T.Order Flow Composition and Trading Costs in a Dynamic Limit Order Market.” Journal of Financial Markets, 2 (1999), 99134.CrossRefGoogle Scholar
Gonzalo, J., and Granger, C. W. J.. “Estimation of Common Long-Memory Components inCo-Integrated Systems.” Journal of Business and Economic Statistics, 13 (1995), 2736.Google Scholar
Grammig, J.; Melvin, M.; and Schlag, C.. “Internationally Cross-Listed Stock Prices During Overlapping Trading Hours: Price Discovery and Exchange Rate Effects.” Journal of Empirical Finance, 12 (2005), 139164.CrossRefGoogle Scholar
Harris, F. H. d.; McInish, T. H.; and Wood, R. A.. “Security Price Adjustment Across Exchanges: An Investigation of Common Factor Components for Dow Stocks.” Journal of Financial Markets,5 (2002), 277308.CrossRefGoogle Scholar
Hasbrouck, J. “One Security, Many Markets: Determining the Contributions to Price Discovery.” Journal of Finance, 50 (1995), 11751199.CrossRefGoogle Scholar
Hasbrouck, J. “Stalking the Efficient Price in Market Microstructure Specifications: An Overview.” Journal of Financial Markets, 5 (2002), 329339.CrossRefGoogle Scholar
Hasbrouck, J. “Intraday Price Formation in U.S. Equity Markets.” Journal of Finance, 58 (2003), 23752399.CrossRefGoogle Scholar
Hautsch, N. Modelling Irregularly Spaced Financial Data: Theory and Practice of Dynamic Duration Models. Heidelberg, New York: Springer (2004).CrossRefGoogle Scholar
Huang, R. D. “The Quality of ECN and Nasdaq Market Maker Quotes.” Journal of Finance, 57 (2002), 15406261.CrossRefGoogle Scholar
Hull, J. C., and White, A.. “Valuing Credit Default Swaps I: No Counterparty Default Risk.” Journal of Derivatives, 8:1 (2000), 2940.CrossRefGoogle Scholar
Hull, J. C., and White, A.. “Valuing Credit Default Swaps II: Modeling Default Correlations.” Journal of Derivatives, 8:3 (2001), 1222.CrossRefGoogle Scholar
Hupperets, E. C., and Menkveld, A. J.. “Intraday Analysis of Market Integration: Dutch Blue Chips Traded in Amsterdam and New York.” Journal of Financial Markets, 5 (2002), 5782.CrossRefGoogle Scholar
Johansen, S. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press (1995).CrossRefGoogle Scholar
Kehrle, K., and Peter, F.. “Who Moves First? An Intensity-Based Measure for Information Flows Across Stock Exchanges.” Journal of Banking and Finance, 37 (2013), 16291642.CrossRefGoogle Scholar
Lanne, M., and Lütkepohl, H.. “Structural Vector Autoregression with Nonnormal Residuals.” Journal of Business and Economic Statistics, 28 (2010), 159168.CrossRefGoogle Scholar
Lanne, M.; Lütkepohl, H.; and Maciejowska, K.. “Structural Vector Autoregressions with Markov Switching.” Journal of Economic Dynamics and Control, 34 (2010), 121131.CrossRefGoogle Scholar
Lehmann, B. N. “Some Desiderata for the Measurement of Price Discovery Across Markets.” Journal of Financial Markets, 5 (2002), 259276.CrossRefGoogle Scholar
Lien, D., and Shrestha, K.. “A New Information Share Measure.” Journal of Futures Markets, 29 (2009), 377395.CrossRefGoogle Scholar
Longin, F., and Solnik, B.. “Extreme Correlation of International Equity Markets.” Journal of Finance, 56 (2001), 649676.CrossRefGoogle Scholar
Lütkepohl, H. New Introduction to Multiple Time Series Analysis. Berlin, New York: Springer (2005).CrossRefGoogle Scholar
MacKinnon, J. “Bootstrap Inference in Econometrics.” Canadian Journal of Economics, 29 (2002), 305325.CrossRefGoogle Scholar
Mizrach, B., and Neely, C.. “Information Shares in the U.S. Treasury Market.” Journal of Banking and Finance, 32 (2008), 12211233.CrossRefGoogle Scholar
Rigobon, R. “Identification through Heteroskedasticity.” Review of Economics and Statistics, 85 (2003), 777792.CrossRefGoogle Scholar
Theissen, E. “Price Discovery in Spot and Futures Markets: A Reconsideration.” European Journal of Finance, 18 (2012), 969987.CrossRefGoogle Scholar
Uhrig-Homburg, M., and Wagner, M.. “Futures Price Dynamics of CO2 Emission Allowance of the Trial Period: An Empirical Analysis.” Journal of Derivatives, 17 (2009), 7388.CrossRefGoogle Scholar
Vlaar, P. “On the Asymptotic Distribution of Impulse Response Functions with Long-Run Restrictions.” Review of Economics and Statistics, 20 (2004), 891903.Google Scholar
Yan, B., and Zivot, E.. “A Structural Analysis of Price Discovery Measures.” Journal of Financial Markets, 13 (2010), 119.CrossRefGoogle Scholar