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Fokker-Planck and Chapman-Kolmogorov Equations for Ito Processes with Finite Memory

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Abstract

The usual derivation of the Fokker-Planck partial differential eqn. (pde) assumes the Chapman-Kolmogorov equation for a Markov process [1, 2]. Starting instead with an Ito stochastic differential equation (sde), we argue that finitely many states of memory are allowed in Kolmogorov’s two pdes, K1 (the backward time pde) and K2 (the Fokker-Planck pde), and show that a Chapman-Kolmogorov eqn. follows as well. We adapt Friedman’s derivation [3] to emphasize that finite memory is not excluded. We then give an example of a Gaussian transition density with 1-state memory satisfying both K1, K2, and the Chapman-Kolmogorov eqns. We begin the paper by explaining the meaning of backward time diffusion, and end by using our interpretation to produce a very short proof that the Green function for the Black-Scholes pde describes a Martingale in the risk neutral discounted stock price.

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References

  1. B.V. Gnedenko, The Theory of Probability, tr. by B.D. Seckler (Chelsea, NY, 1967).

    Google Scholar 

  2. R.L. Stratonovich. Topics in the Theory of Random Noise, tr. by R.A. Silverman (Gordon & Breach, NY, 1963).

    Google Scholar 

  3. A. Friedman, Stochastic Differential Equations and Applications (Academic, NY, 1975).

    MATH  Google Scholar 

  4. L. Arnold, Stochastic Differential Equations (Krieger, Malabar, 1992).

    Google Scholar 

  5. J.M. Steele, Stochastic Calculus and Financial Applications (Springer-Verlag, NY, 2000).

    Google Scholar 

  6. K.E. Bassler, G.H. Gunaratne, & J.L. McCauley, Hurst Exponents, Markov Processes, and Nonlinear Diffusion Equations, Physica A 369: 343 (2006).

    Article  ADS  MathSciNet  Google Scholar 

  7. J.L. McCauley, G.H. Gunaratne, & K.E. Bassler, Martingales, Detrending Data, and the Efficient Market Hypothesis, Physica A 387: 3916–3920 (2008).

    Article  ADS  MathSciNet  Google Scholar 

  8. P. Hänggi & H. Thomas, Time Evolution, Correlations, and Linear Response of Non-Markov Processes, Zeitschr. Für Physik B26: 85 (1977).

    ADS  Google Scholar 

  9. J.L. McCauley, Markov vs. nonMarkovian Processes: A Comment on the Paper ‘Stochastic Feedback, Nonlinear Families of Markov Processes, and Nonlinear Fokker-Planck Equations, by T.D. Frank, Physica A 382: 445–452 (2007).

    Article  ADS  Google Scholar 

  10. J.L. McCauley, G.H. Gunaratne, & K.E. Bassler, Hurst Exponents, Markov Processes, and Fractional Brownian Motion, Physica A 379: 1–9 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  11. P. Hänggi, H. Thomas, H. Grabert, & P. Talkner, Note on time Evolution of Non-Markov Processes, J. Stat. Phys. 18: 155 (1978).

    Article  ADS  Google Scholar 

  12. T.D. Frank, Stochastic Feedback, Nonlinear Families of Markov Processes, and Nonlinear Fokker-Planck Equations, Physica A 331: 391 (2004).

    Article  ADS  MathSciNet  Google Scholar 

  13. J.L. McCauley, Dynamics of Markets: Econophysics and Finance (Cambridge, Cambridge, 2004).

    Book  MATH  Google Scholar 

  14. J.L. McCauley, G.H. Gunaratne, & K.E. Bassler, Martingale Option Pricing, Physica A 380: 351–356 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  15. M.C. Wang & G.E. Uhlenbeck in Selected Papers on Noise and Stochastic Processes, ed. by N. Wax (Dover, NY, 1954).

    Google Scholar 

  16. D. Duffie, An Extension of the Black-Scholes Model of Security Valuation, J. Econ. Theory 46, 194, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  17. W. Feller, The Annals of Math. Statistics 30, No. 4, 1252, 1959.

    MATH  MathSciNet  Google Scholar 

  18. J.L. Snell, A Conversation with Joe Doob, http://www.dartmouth.edu/~chance/Doob/conversation.html; Statistical Science 12, No. 4, 301, 1997.

    Article  MATH  MathSciNet  Google Scholar 

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McCauley, J.L. (2008). Fokker-Planck and Chapman-Kolmogorov Equations for Ito Processes with Finite Memory. In: Skjeltorp, A.T., Belushkin, A.V. (eds) Evolution from Cellular to Social Scales. NATO Science for Peace and Security Series B: Physics and Biophysics. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8761-5_8

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