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Markov properties of high frequency exchange rate data


  • C. Renner

    (University of Oldenburg)

  • J. Peinke

    (University of Oldenburg)

  • R. Friedrich

    (University of Stuttgart)


We present a stochastic analysis of a data set consisiting of 10^6 quotes of the US Doller - German Mark exchange rate. Evidence is given that the price changes x(tau) upon different delay times tau can be described as a Markov process evolving in tau. Thus, the tau-dependence of the probability density function (pdf) p(x) on the delay time tau can be described by a Fokker-Planck equation, a gerneralized diffusion equation for p(x,tau). This equation is completely determined by two coefficients D_{1}(x,tau) and D_{2}(x,tau) (drift- and diffusion coefficient, respectively). We demonstrate how these coefficients can be estimated directly from the data without using any assumptions or models for the underlying stochastic process. Furthermore, it is shown that the solutions of the resulting Fokker-Planck equation describe the empirical pdfs correctly, including the pronounced tails.

Suggested Citation

  • C. Renner & J. Peinke & R. Friedrich, 2001. "Markov properties of high frequency exchange rate data," Papers cond-mat/0102494,, revised Apr 2001.
  • Handle: RePEc:arx:papers:cond-mat/0102494

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    References listed on IDEAS

    1. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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    Cited by:

    1. Ausloos, Marcel & Ivanova, Kristinka & Siwy, Zuzanna, 2004. "Searching for self-similarity in switching time and turbulent cascades in ion transport through a biochannel. A time delay asymmetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 319-333.
    2. G. L. Buchbinder & K. M. Chistilin, 2006. "Multiple time scales and the empirical models for stochastic volatility," Papers physics/0611048,
    3. Buchbinder, G.L. & Chistilin, K.M., 2007. "Multiple time scales and the empirical models for stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 168-178.

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