Quasi-Monte Carlo methods in numerical finance
Article Abstract:
A new version of the Monte Carlo method that can be used for the numerical valuation of derivatives is presented. The conventional Monte Carlo method uses pseudo-random numbers to evaluate the expression of interest which, unfortunately, yields an error bound that is probabilistic. The standard approach usually requires many simulations to get a high level of accuracy. The new approach promises to be very useful in numerical finance, such as the valuation of derivatives. Quasi-Monte Carlo methods use deterministic rather than random sequences resulting in better convergence and lower deterministic error bounds. Traditional variance reduction techniques can also be used to improve the performance of quasi-Monte Carlo methods. Furthermore, accuracy can be enhanced by applying Richardson extrapolation in conjunction with quasi-Monte Carlo methods.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1996
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The impact of autocorrelation on queueing systems
Article Abstract:
The impact of autocorrelation on queue performance is examined in the context of independent interarrival intervals and service demands. Common in actual systems, autocorrelation given such conditions leads to analytical intractability and the simplification of independence assumptions is likely to worsen performance estimates. Simulations using two computer techniques for thegeneration of autocorrelated random sequences show that performance measures degenerate with the introduction of autocorrelation into both interarrival times and into service demands, although to a lesser extent in the latter. The extent of these deleterious autocorrelative effects is determined by the type of generation method used for the autocorrelation.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1993
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