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Empirical market microstructure: An analysis of the BRL/US$ exchange rate market

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  • Laurini, Márcio Poletti
  • Furlani, Luiz Gustavo Cassilatti
  • Portugal, Marcelo Savino

Abstract

This article provides an analysis of empirical microstructure for the BRL/US$ exchange rate market using high-frequency bid and ask quote data. The aims of the article are to verify the importance of the presence of asymmetric information in price dynamics, to build a model for the price discovery process and to analyze the empirical determinants of the spread between bid and ask through a conditional model that captures an asymmetric response to the spread regarding past information. The asymmetric information hypothesis is tested through a nonparametric test of conditional independence for the Markov property. A model for price discovery is built using a vector error correction between bid and ask, controlling for duration and volatility. As a result of this vector, we build an equilibrium spread deviation series, and we show that the conditional distribution of equilibrium spread deviations responds asymmetrically to the spread changes and expected conditional volatilities and durations. This is made by using the quantilogram and a quantile autoregression as tools for modeling the asymmetry effects. We relate the findings to some facts presented in the theoretical literature on market microstructure.

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Bibliographic Info

Article provided by Elsevier in its journal Emerging Markets Review.

Volume (Year): 9 (2008)
Issue (Month): 4 (December)
Pages: 247-265

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Handle: RePEc:eee:ememar:v:9:y:2008:i:4:p:247-265

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Web page: http://www.elsevier.com/locate/inca/620356

Related research

Keywords: Market microstructure Emerging market Spread Markov property Asymmetric response Quantile regression;

References

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  1. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
  2. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
  3. Fernandes, M. & Grammig, J., 2000. "Non-Parametric Specification Tests for Conditional Duration Models," Economics Working Papers eco2000/4, European University Institute.
  4. Fernandes, Marcelo & Grammig, Joachim, 2002. "A Family of Autoregressive Conditional Duration Models," Economics Working Papers (Ensaios Economicos da EPGE) 440, Graduate School of Economics, Getulio Vargas Foundation (Brazil).
  5. Hasbrouck, Joel, 1988. "Trades, quotes, inventories, and information," Journal of Financial Economics, Elsevier, vol. 22(2), pages 229-252, December.
  6. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  7. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275.
  8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  9. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.
  10. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
  11. Harrison, J. Michael & Kreps, David M., 1979. "Martingales and arbitrage in multiperiod securities markets," Journal of Economic Theory, Elsevier, vol. 20(3), pages 381-408, June.
  12. Harrison, J. Michael & Pliska, Stanley R., 1981. "Martingales and stochastic integrals in the theory of continuous trading," Stochastic Processes and their Applications, Elsevier, vol. 11(3), pages 215-260, August.
  13. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
  14. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
  15. Bollerslev, Tim & Domowitz, Ian, 1993. " Trading Patterns and Prices in the Interbank Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 48(4), pages 1421-43, September.
  16. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
  17. Flood, Mark D., 1994. "Market structure and inefficiency in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 13(2), pages 131-158, April.
  18. Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2004. "A comparison of financial duration models via density forecast," ULB Institutional Repository 2013/136218, ULB -- Universite Libre de Bruxelles.
  19. Amaro de Matos, Joao & Fernandes, Marcelo, 2007. "Testing the Markov property with high frequency data," Journal of Econometrics, Elsevier, vol. 141(1), pages 44-64, November.
  20. Bingcheng Yan & Eric Zivot, 2003. "Analysis of High-Frequency Financial Data with S-PLUS," Working Papers UWEC-2005-03, University of Washington, Department of Economics.
  21. Mark P. Taylor, 1995. "The Economics of Exchange Rates," Journal of Economic Literature, American Economic Association, vol. 33(1), pages 13-47, March.
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