Skip to main content
Log in

Parameter Estimation for Multiscale Diffusions

  • Published:
Journal of Statistical Physics Aims and scope Submit manuscript

Abstract

We study the problem of parameter estimation for time-series possessing two, widely separated, characteristic time scales. The aim is to understand situations where it is desirable to fit a homogenized single-scale model to such multiscale data. We demonstrate, numerically and analytically, that if the data is sampled too finely then the parameter fit will fail, in that the correct parameters in the homogenized model are not identified. We also show, numerically and analytically, that if the data is subsampled at an appropriate rate then it is possible to estimate the coefficients of the homogenized model correctly.

The ideas are studied in the context of thermally activated motion in a two-scale potential. However the ideas may be expected to transfer to other situations where it is desirable to fit an averaged or homogenized equation to multiscale data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Y. Ait-Sahalia, P. A. Mykland and L. Zhang, How often to sample a continuous-time process in the presence of market microstructure noise. Rev. Financ. Studies 18:351–416 (2005).

    Article  Google Scholar 

  2. Y. Ait-Sahalia, P. A. Mykland and L. Zhang, A tale of two time scales: Determining integrated volatility with noisy high-frequency data. J. Amer. Stat. Assoc. 100:1394–1411 (2005).

    Article  MathSciNet  Google Scholar 

  3. O. E. Barndorff-Nielsen, P. R. Hansen, A. Lunde and N. Shephard, Designing realised kernels to measure the ex-post variation of equity in the presence of noise. Preprint (2006).

  4. I. V. Basawa and B. L. S. Prakasa Rao, Statistical inference for stochastic processes. Academic Press Inc. [Harcourt Brace Jovanovich Publishers] (London, 1980).

  5. A. Bensoussan, J. L. Lions and G. Papanicolaou, Asymptotic analysis of periodic structures. (North-Holland, Amsterdam, 1978).

  6. C. P. Calderon, Fitting effective diffusion models to data associated with a glassy potential: Estimation, classical inference procedures and some heuristics. SIAM Multiscale Modeling and Simulation,to appear.

  7. F. Campillo and A. Piatnitski, Effective diffusion in vanishing viscosity. In Nonlinear partial differential equations and their applications. Collège de France Seminar, Vol. XIV (Paris, 1997/1998), volume 31 of Stud. Math. Appl. (pp. 133–145, North-Holland, Amsterdam, 2002).

  8. D. Cioranescu and P. Donato, An Introduction to Homogenization. (Oxford University Press, New York, 1999).

    MATH  Google Scholar 

  9. D. T. Crommelin and E. Vanden-Eijnden, Reconstruction of diffusions using spectral data from timeseries. Commun. Math. Sci. 4(3):651–668 (2006).

    MATH  MathSciNet  Google Scholar 

  10. W. E. D. Liu and E. Vanden-Eijnden, Analysis of multiscale methods for stochastic differential equations. Comm. Pure Appl. Math. 58(11):1544–1585 (2005).

    Article  MATH  MathSciNet  Google Scholar 

  11. J.-P. Fouque, G. Papanicolaou R. Sircar, and K. Solna, Short time scale in S and P volatility. J. Comp. Finance 6(4):1–23 (2003).

    Google Scholar 

  12. J.-P. Fouque, G. C. Papanicolaou, and R. K. Sircar, Derivatives in financial markets with stochastic volatility. (Cambridge University Press, Cambridge, 2000).

    MATH  Google Scholar 

  13. M. Freidlin, Functional integration and partial differential equations, volume 109 of Annals of Mathematics Studies. (Princeton University Press, Princeton, NJ, 1985).

  14. D. Givon, I. G. Kevrekidis and R. Kupferman, Strong convergence schemes of projective intregration schemes for singularly perturbed stochastic differential equations. Comm. Math. Sci. 4(4):707–729 (2006).

    MathSciNet  Google Scholar 

  15. D. Givon, R. Kupferman and A. M. Stuart, Extracting macroscopic dynamics: Model problems and algorithms. Nonlinearity 17(6):R55–R127 (2004).

    Article  MATH  ADS  MathSciNet  Google Scholar 

  16. M. Hairer and G. A. Pavliotis, Periodic homogenization for hypoelliptic diffusions. J. Statist. Phys. 117(1–2):261–279 (2004).

    Article  MATH  MathSciNet  Google Scholar 

  17. G. Humer and I. G. Kevrekidis, Coarse molecular dynamics of a peptide fragment: Free energy, kinetics and long time dynamics computations. J. Chem. Phys. 118(23):10762–10773 (2003).

    Article  ADS  Google Scholar 

  18. I. Karatzas and S. E. Shreve, Brownian motion and stochastic calculus, volume 113 of Graduate Texts in Mathematics. (Springer-Verlag, New York, second edition 1991).

  19. P. E. Kloeden and E. Platen, Numerical solution of stochastic differential equations, volume 23 of Applications of Mathematics (New York). (Springer-Verlag, Berlin, 1992).

  20. R. S. Liptser and A. N. Shiryaev, Statistics of random processes. I, volume 5 of Applications of Mathematics (New York). (Springer-Verlag, Berlin, 2001).

  21. X. Mao, Stochastic differential equations and their applications. (Horwood Publishing Series in Mathematics & Applications. Horwood Publishing Limited, Chichester, 1997).

  22. J. C. Mattingly, A. M. Stuart and D. J. Higham, Ergodicity for SDEs and approximations: Locally Lipschitz vector fields and degenerate noise. Stochastic Process. Appl. 101(2):185–232 (2002).

    Article  MATH  MathSciNet  Google Scholar 

  23. S. Olla, Homogenization of diffusion processes in random fields. (Lecture Notes, 1994).

  24. E. Pardoux, Homogenization of linear and semilinear second order parabolic pdes with periodic coefficients: A probabilistic approach. J. Funct. Anal. 167:498–520 (1999).

    Article  MATH  MathSciNet  Google Scholar 

  25. G. A. Pavliotis and A. M. Stuart, An introduction to Multiscale Methods: Averaging and Homogenization (Lecture Notes, 2006).

  26. D. Revuz and M. Yor, Continuous martingales and Brownian motion, volume 293 of Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]. (Springer-Verlag, Berlin, third edition, 1999).

  27. E. Vanden-Eijnden, Numerical techniques for multi-scale dynamical systems with stochastic effects. Commun. Math. Sci. 1(2):385–391 (2003).

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. A. Pavliotis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pavliotis, G.A., Stuart, A.M. Parameter Estimation for Multiscale Diffusions. J Stat Phys 127, 741–781 (2007). https://doi.org/10.1007/s10955-007-9300-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10955-007-9300-6

Keywords

Navigation