Financial Engineering Seminar
October 27, 2008 (Mon)
Time: 4:05 PM
Place: CSE E122
Quasi-Monte Carlo simulation, which uses low-discrepancy sequences instead of pseudorandom numbers, is becoming increasingly popular in applications, in particular, economics and finance. Since the normal distribution occurs frequently in economic and financial modeling, one often needs a method to transform low-discrepancy sequences from the uniform distribution to the normal distribution. Two well known methods used with pseudorandom numbers are the Box-Muller method and the inverse transformation method.
Some researchers and financial engineers have claimed that it is incorrect to use the Box-Muller method with low-discrepancy sequences, and instead, the inverse transformation method should be used. In this talk I will argue that, in fact, the opposite is true, by presenting numerical examples, discussing a test for randomness for low-discrepancy sequences, and by providing a theoretical justification for the Box-Muller method in the context of low-discrepancy sequences.