Combining Latin Hypercube Sampling with Other Variance Reduction Techniques
18 Pages Posted: 22 Nov 2012
Date Written: November 2, 2012
We consider the problem of reducing the variance of Monte Carlo estimators of multivariate estimation problems by combining the variance reduction techniques Latin hypercube sampling with dependence (LHSD), control variates and importance sampling. Under some standard conditions, the resulting estimators are consistent and asymptotically unbiased, and a central limit theorem holds. The effectiveness of the combined variance reduction methods is investigated by pricing an Asian basket call option, which is a high-dimensional problem. When comparing the effectiveness with existing combined variance reduction techniques, it turns out that techniques highly tailored to the specific problem are more effective, but among the methods that make no use of specific information, LHSD performs best.
Keywords: Monte Carlo simulation, variance reduction, Latin hypercube sampling (with dependence), control variates, importance sampling, derivative pricing
JEL Classification: C15, C63, G13
Suggested Citation: Suggested Citation