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Heterogeneous real-time trading strategies in the foreign exchange market

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Author Info

  • M. M. Dacorogna
  • U. A. Muller
  • C. Jost
  • O. V. Pictet
  • J. R. Ward

Abstract

The foreign exchange (FX) market is worldwide, but the dealers differ in their geographical locations (time zones), working hours, time horizons, home currencies, access to information,transaction costs, and other institutional constraints. The variety of time horizones is large: from intra-day dealers, who close their positions every evening, to long-term investors and central banks. Depending on the constraints, the different market participats need different strategies to reach their goal, which is usually maximizing the profit, or rather a utility function including risk. Different intra-day trading strategies can be studied only if high-density data are available. Oslen & Associates (O & A) has collected and analysed large amounts of FX quotes by market makers around the clock (up to 5000 non-equally spaced prices per day for the German mark against US$). Based on these data, a set of real-time intra-day trading models has been developed. These models give explicit trading recommendations under realistic constraints. They are allowed to trade only during the opening hours of a market, depending on the time zone and local holidays. The models have been running real-time for more than three years, thus leading to an ex ante test. The test results, obtained with a risk-sensitive performance measure, are presented. All these trading models are profitable, but they differ in their risk behaviour and dealing frequency. If a certain profitable intra-day algorithm is tested with different working hours, its success can considerably change. A systematic study shows that the best choice of working hours is usually when the most important markets for the particular FX rate are active. All the results demonstrate that the assumption of a homogeneous 24-hour FX market with identical dealers, following an identical 'rational expectation', is far from reality. To explain the market dynamics, a heterogeneous model of the market with different types of dealers is more appropriate.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

Volume (Year): 1 (1995)
Issue (Month): 4 ()
Pages: 383-403

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Handle: RePEc:taf:eurjfi:v:1:y:1995:i:4:p:383-403

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Related research

Keywords: foreign exchange market; trading model; heterogeneous expectations;

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Cited by:
  1. Hommes, C.H., 2001. "Modeling the stylized facts in finance through simple nonlinear adaptive systems," CeNDEF Working Papers 01-06, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  2. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
  3. Youwei Li & Xue-Zhong (Tony) He, 2005. "Heterogeneity, Profitability and Autocorrelations," Computing in Economics and Finance 2005 244, Society for Computational Economics.
  4. Cars H. Hommes, 2005. "Heterogeneous Agent Models in Economics and Finance," Tinbergen Institute Discussion Papers 05-056/1, Tinbergen Institute.
  5. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
  6. Xue-Zhong He, 2003. "Asset Pricing, Volatility and Market Behaviour: A Market Fraction Approach," Research Paper Series 95, Quantitative Finance Research Centre, University of Technology, Sydney.
  7. Anufriev Mikhail & Bottazzi Giulio, 2012. "Asset Pricing with Heterogeneous Investment Horizons," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-38, October.
  8. Kyrtsou, Catherine & Terraza, Michel, 2002. "Stochastic chaos or ARCH effects in stock series?: A comparative study," International Review of Financial Analysis, Elsevier, vol. 11(4), pages 407-431.
  9. Cars Hommes, 2005. "Heterogeneous Agent Models: Two Simple Case Studies," Tinbergen Institute Discussion Papers 05-055/1, Tinbergen Institute.
  10. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
  11. Carl Chiarella & Xue-Zhong He & Duo Wang, 2004. "Statistical Properties of a Heterogeneous Asset Price Model with Time-Varying Second Moment," Research Paper Series 142, Quantitative Finance Research Centre, University of Technology, Sydney.
  12. Xue-Zhong He & Youwei Li, 2005. "Long Memory, Heterogeneity and Trend Chasing," Research Paper Series 148, Quantitative Finance Research Centre, University of Technology, Sydney.
  13. repec:att:wimass:9706 is not listed on IDEAS
  14. Hommes, C.H., 2005. "Heterogeneous Agents Models: two simple examples, forthcoming In: Lines, M. (ed.) Nonlinear Dynamical Systems in Economics, CISM Courses and Lectures, Springer, 2005, pp.131-164," CeNDEF Working Papers 05-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  15. Gencay, Ramazan & Dacorogna, Michel & Olsen, Richard & Pictet, Olivier, 2003. "Foreign exchange trading models and market behavior," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 909-935, April.
  16. Cars Hommes, 2005. "Heterogeneous Agent Models: Two Simple Case Studies," Tinbergen Institute Discussion Papers 05-055/1, Tinbergen Institute.

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