Mathematical Modeling in Finance with Stochastic Processes

200 CHAPTER 6. STOCHASTIC CALCULUS

Of course, this can be programmed and the step size made much smaller,
presumably with better approximation properties. In fact, it is possible to
consider kinds of convergence for the EM method comparable to the Strong
Law of Large Numbers and the Weak Law of Large Numbers.

Sources

This section is adapted from: “An Algorithmic Introduction to the Numerical
Simulation of Stochastic Differential Equations”, by Desmond J. Higham, in
SIAM Review, Vol. 43, No. 3, pp. 525-546, 2001 andFinancial Calculus: An
introduction to derivative pricingby M Baxter, and A. Rennie, Cambridge
University Press, 1996, pages 52-62.

Problems to Work for Understanding

1. Simulate the solution of the stochastic differential equation

dX(t) =X(t)dt+ 2X(t)dX

on the interval [0,1] with initial condition X(0) = 1 and step size

∆t= 1/10.

2. Simulate the solution of the stochastic differential equation

dX(t) =tX(t)dt+ 2X(t)dX

on the interval [0,1] with initial condition X(0) = 1 and step size

∆t= 1/10. Note the difference with the previous problem, now the

multiplier of thedtterm is a function of time.

Outside Readings and Links:

1. Maple Stochastic Package The MAPLE stochastic package offers a
   number of MAPLE routines for stochastic differential equations.


2. Matlab program files for Stochastic Differential Equations offers a num-
   ber of MATLAB routines for stochastic differential equations.