An empirical evaluation of sampling methods in risk analysis simulation: quasi-Monte Carlo, descriptive sampling, and latin hypercube sampling

This paper compares the performance, in terms of convergence rates and precision of the estimates, for six Monte Carlo simulation sampling methods: quasi-Monte Carlo using Halton, Sobol, and Faure numeric sequences; descriptive sampling, based on the use of deterministic sets and Latin hypercube sampling, based on stratified numerical sets. Those methods are compared to the classical Monte Carlo. The comparison was made for two basic risky applications: the first one evaluates the risk in a decision making process when launching a new product; the second evaluates the risk of accomplishing an expected rate of return in a correlated stock portfolio. Descriptive sampling and Latin hypercube sampling have shown the best aggregate results.