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IFSM Representation of Brownian Motion with Applications to Simulation

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Math Everywhere

Abstract

Several methods are currently available to simulate paths of the Brownian motion. In particular, paths of the BM can be simulated using the properties of the increments of the process like in the Euler scheme, or as the limit of a random walk or via L 2 decomposition like the Kac-Siegert/Karnounen-Loeve series. In this paper we first propose a IFSM (Iterated Function Systems with Maps) operator whose fixed point is the trajectory of the BM. We then use this representation of the process to simulate its trajectories. The resulting simulated trajectories are self-affine, continuous and fractal by construction. This fact produces more realistic trajectories than other schemes in the sense that their geometry is closer to the one of the true BM’s trajectories. The IFSM trajectory of the BM can then be used to generate more realistic solutions of stochastic differential equations.

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References

  1. Byrd, R.H., Lu, P., Nocedal, J. and Zhu, C. (1995), “A limited memory algorithm for bound constrained optimization”, SIAM J. Scientific Computing, 16, 1190–1208.

    Article  MATH  Google Scholar 

  2. Forte, B., Vrscay, E.R. (1995), “Solving the inverse problem for function/image approximation using iterated function systems, I. Theoretical basis”, Fractal, 2, 3, 325–334.

    Article  Google Scholar 

  3. Kloden, P., Platen, E., Shurtz, H. (2000), Numerical Solution of SDE through computer experiments, Springer, Berlin.

    Google Scholar 

  4. Shorack, G., Wellner, J.A. (1986), Empirical processes with applications to statistics, Wiley, New York.

    Google Scholar 

  5. R Development Core Team (2005), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org

    Google Scholar 

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© 2007 Springer

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Iacus, S.M., Torre, D.L. (2007). IFSM Representation of Brownian Motion with Applications to Simulation. In: Aletti, G., Micheletti, A., Morale, D., Burger, M. (eds) Math Everywhere. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44446-6_10

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