Combining Latin Hypercube Sampling with Other Variance Reduction Techniques

18 Pages Posted: 22 Nov 2012

See all articles by Natalie Packham

Natalie Packham

Berlin School of Economics and Law; Humboldt University Berlin

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

Packham, Natalie, Combining Latin Hypercube Sampling with Other Variance Reduction Techniques (November 2, 2012). Available at SSRN: or

Natalie Packham (Contact Author)

Berlin School of Economics and Law ( email )

Badensche Strasse 50-51
Berlin, D-10825


Humboldt University Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099

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