Monte Carlo simulation is widely used to value complex financial instruments. An alternative to Monte Carlo is to use "low discrepancy" methods. Theory suggests that low discrepancy methods might be superior to the Monte Carlo method. We compared the performance of low discrepancy methods with Monte Carlo on a Collateralized Mortgage Obligation (CMO) with ten tranches. We found that a particular low discrepancy method based on Sobol points consistently outperforms Monte Carlo. Although our tests were for a CMO, we believe it will be advantageous to use the Sobol method for many other types of instruments. We have made major improvements in published routines for generating Sobol points which we have embedded in a software system called FINDER.
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