Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T12:21:51.838Z Has data issue: false hasContentIssue false

A Variable Step Size Riemannian Sum for an Itô Integral

Published online by Cambridge University Press:  14 July 2016

E. Rapoo*
Affiliation:
University of South Africa
*
Postal address: Department of Mathematical Sciences, University of South Africa, PO Box 392, UNISA 0003, Republic of South Africa. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We investigate the problem of using a Riemannian sum with random subintervals to approximate the iterated Itô integral ∫wdw - or, equivalently, solving the corresponding stochastic differential equation by Euler's method with variable step sizes. In the past this task has been used as a counterexample to illustrate that variable step sizes must be used with extreme caution in stochastic numerical analysis. This article establishes a class of variable step size schemes which do work.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2008 

References

[1] Bichteler, K. (1981). Stochastic integration and L{p} theory of semimartingales. Ann. Prob. 9, 4989.Google Scholar
[2] Bouleau, N. and Lépingle, D. (1994). Numerical Methods for Stochastic Processes. John Wiley, New York.Google Scholar
[3] Burrage, K., Burrage, P.M. and Tian, T. (2004). Numerical methods for strong solutions of stochastic differential equations: an overview. Proc. R. Soc. London 460, 373402.Google Scholar
[4] Burrage, P. M. and Burrage, K. (2002). A variable stepsize implementation for stochastic differential equations. SIAM J. Sci. Comput. 24, 848864.Google Scholar
[5] Burrage, P.M., Herdiana, R. and Burrage, K. (2004). Adaptive stepsize based on control theory for stochastic differential equations. J. Comput. Appl. Math. 170, 317336.Google Scholar
[6] Gaines, J. G. and Lyons, T. L. (1994). Random generation of stochastic area integrals. SIAM J. Appl. Math. 54, 11321146.CrossRefGoogle Scholar
[7] Gaines, J. G. and Lyons, T. L. (1997). Variable step size control in the numerical solution of stochatic differential equations. SIAM J. Appl. Math. 57, 14551484.Google Scholar
[8] Hofmann, N., Müller-Gronbach, T. and Ritter, K. (2000). Optimal approximation of stochastic differential equations by adaptive step-size control. Math. Comput. 69, 10171034.Google Scholar
[9] Hofmann, N., Müller-Gronbach, T. and Ritter, K. (2001). The optimal discretization of stochastic differential equations. J. Complexity 17, 117153.Google Scholar
[10] Ikeda, N. and Watanabe, S. (1981). Stochastic Differential Equations and Diffusion processes. North-Holland, Amsterdam.Google Scholar
[11] Kloeden, P. E. and Platen, E. (1992). Numerical Solution of Stochastic Differential Equations. Springer, Berlin.CrossRefGoogle Scholar
[12] Lamba, H., Mattingly, J. C. and Stuart, A. M. (2007). An adaptive Euler–Maruyama scheme for SDEs: convergence and stability. IMA J. Numerical Anal. 27, 479506.Google Scholar
[13] Lehn, J., Rössler, A. and Schein, O. (2002). Adaptive schemes for the numerical solution of SDEs – a comparison. J. Comput. Appl. Math. 138, 297308.Google Scholar
[14] Mauthner, S. (1998). Step size control in the numerical solution of stochastic differential equations. J. Comput. Appl. Math. 100, 93109.Google Scholar
[15] Milstein, G. N. and Tretyakov, M. V. (1997). Mean-square numerical methods for stochastic differential equations with small noises. SIAM J. Sci. Comput. 18, 10671087.CrossRefGoogle Scholar
[16] Newton, N. J. (1990). An efficient approximation for stochastic differential equations on the partition of symmetrical first passage times. Stoch. Stoch Reports 29, 227258.Google Scholar
[17] Revuz, D. and Yor, M. (1994). Continuous Martingales and Brownian Motion, 2nd edn. Springer, Berlin.Google Scholar
[18] Römisch, W. and Winkler, R. (2006). Stepsize control for mean-square numerical methods for stochastic differential equations with small noise. SIAM J. Sci. Comput. 28, 604625.Google Scholar
[19] Szepessy, A., Tempone, R. and Zouraris, G. E. (2001). Adaptive weak approximation of stochastic differential equations. Commun. Pure Appl. Math. 54, 11691214.Google Scholar
[20] Talay, D. (1995). Simulation of stochastic differential systems. In Probabilistic Methods in Applied Physics (Lecture Notes Physics 451), eds Kree, P. and Wedig, W., Springer, Berlin, pp. 5496.Google Scholar