Generation of correlated pseudorandom variables in Monte Carlo simulation

Correlated pseudorandom variables with prescribed marginal distribution functions sometimes are required in simulation such as in Monte Carlo studies. In this paper, we present a general procedure and a simple but effective numerical approach to generating correlated random variables sampling sequence with prescribed marginal probability distribution functions and correlation coefficient matrix based on linear transformation-nonlinear transformation with Choesky factor. Some simulation results are reported. Simulation results show that the collections of random numbers generated by the presented procedure have desired correlations and pass the Kolmogorov-Smirnov non-parametric hypothesis test of specified marginal distribution. Some restrictions on the application of this method are discussed.