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Sim.DiffProc: A Package for Simulation of Diffusion Processes in R

Author

Listed:
  • Kamal Boukhetala

    (Faculté des Mathématiques - USTHB - Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger])

  • Arsalane Guidoum

    (Department of Probabilities & Statistics - USTHB - Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger])

Abstract

The Sim.DiProc package provides a simulation of diffusion processes and the differences methods of simulation of solutions for stochastic differential equations (SDEs) of the Ito's type, in financial and actuarial modeling and other areas of applications, for example the stochastic modeling and simulation of pollutant dispersion in shallow water using the attractive center, and the model of two diffusions in attraction, which can modeling the behavior of two insects, one attracts the other. The simulation of the processes of diffusion, through stochastic differential equations to allow simulated a random variable tc " first passage time" of the particle through a sphere of radius c, two methods are used in the estimation problem of the probability density function of the random variable tc: the histograms and the kernel methods. The R package Sim.DiffProc is introduced, providing a simulation and estimation for the stationary distribution of the stochastic process that describes the equilibrium of some dynamics.

Suggested Citation

  • Kamal Boukhetala & Arsalane Guidoum, 2011. "Sim.DiffProc: A Package for Simulation of Diffusion Processes in R," Working Papers hal-00629841, HAL.
    • Handle: RePEc:hal:wpaper:hal-00629841
    Note: View the original document on HAL open archive server: https://hal.science/hal-00629841
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    References listed on IDEAS

    as
    1. Yoshihiro Saito & Taketomo Mitsui, 1993. "Simulation of stochastic differential equations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(3), pages 419-432, September.
    2. P. E. Kloeden & Eckhard Platen, 1989. "A survey of numerical methods for stochastic differential equations," Published Paper Series 1989-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Attractive model; diffusion process; simulations; stochastic differential equation; stochastic modeling; R language.; R language;
    All these keywords.

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