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Fokker–Planck equation of distributions of financial returns and power laws

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  • Sornette, Didier

Abstract

Our purpose is to relate the Fokker–Planck formalism proposed by [Friedrich et al., Phys. Rev. Lett. 84 (2000) 5224] for the distribution of stock market returns to the empirically well-established power-law distribution with an exponent in the range 3–5. We show how to use Friedrich et al.'s formalism to predict that the distribution of returns is indeed asymptotically a power law with an exponent μ that can be determined from the Kramers–Moyal coefficients determined by Friedrich et al. However, with their values determined for the U.S. dollar–German mark exchange rates, the exponent μ predicted from their theory is found to be around 12, in disagreement with the often-quoted value between 3 and 5. This could be explained by the fact that the large asymptotic value of 12 does not apply to real data that lie still far from the stationary state of the Fokker–Planck description. Another possibility is that power laws are inadequate. The mechanism for the power law is based on the presence of multiplicative noise across time-scales, which is different from the multiplicative noise at fixed time-scales implicit in the ARCH models developed in the Finance literature.

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  • Sornette, Didier, 2001. "Fokker–Planck equation of distributions of financial returns and power laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 290(1), pages 211-217.
  • Handle: RePEc:eee:phsmap:v:290:y:2001:i:1:p:211-217
    DOI: 10.1016/S0378-4371(00)00571-9
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    1. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
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    5. De Vries, C.G. & Leuven, K.U., 1994. "Stylized Facts of Nominal Exchange Rate Returns," Papers 94-002, Purdue University, Krannert School of Management - Center for International Business Education and Research (CIBER).
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    2. Renner, Ch. & Peinke, J. & Friedrich, R., 2001. "Evidence of Markov properties of high frequency exchange rate data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 298(3), pages 499-520.
    3. Alexander Shapovalov & Andrey Trifonov & Elena Masalova, 2008. "Nonlinear Fokker-Planck Equation in the Model of Asset Returns," Papers 0804.0900, arXiv.org.
    4. J. B. Glattfelder & A. Dupuis & R. B. Olsen, 2010. "Patterns in high-frequency FX data: discovery of 12 empirical scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 599-614.
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    6. Kozaki, M. & Sato, A.-H., 2008. "Application of the Beck model to stock markets: Value-at-Risk and portfolio risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1225-1246.

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