NUMERICAL INVESTIGATION OF D-BIFURCATIONS FOR A STOCHASTIC DELAY LOGISTIC EQUATION

2005 ◽  
Vol 05 (02) ◽  
pp. 211-222 ◽  
Author(s):  
NEVILLE J. FORD ◽  
STEWART J. NORTON

This paper explores the use of numerical (approximation) methods in the detection of changes in the dynamical behaviour of solutions to parameter-dependent stochastic delay differential equations. We focus on the use of approximations to Lyapunov exponents. Using three numerical methods we begin to describe the probability distributions of the local approximate Lyapunov exponents and we use this information to enable us to predict values of the parameters at which solutions bifurcate. We conclude the paper by reviewing some of the potential pitfalls of using numerical simulations to detect the dynamical behaviour of the solutions to stochastic delay differential equations.

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yanli Zhou ◽  
Yonghong Wu ◽  
Xiangyu Ge ◽  
B. Wiwatanapataphee

Stochastic delay differential equations with jumps have a wide range of applications, particularly, in mathematical finance. Solution of the underlying initial value problems is important for the understanding and control of many phenomena and systems in the real world. In this paper, we construct a robust Taylor approximation scheme and then examine the convergence of the method in a weak sense. A convergence theorem for the scheme is established and proved. Our analysis and numerical examples show that the proposed scheme of high order is effective and efficient for Monte Carlo simulations for jump-diffusion stochastic delay differential equations.


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