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Reclaiming quasi-Monte Carlo efficiency in portfolio value-at-risk simulation through Fourier transform
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Jin, Xing and Zhang, Allen X. (2006) Reclaiming quasi-Monte Carlo efficiency in portfolio value-at-risk simulation through Fourier transform. Management Science, Vol.52 (No.6). pp. 925-938. doi:10.1287/mnsc.1060.0505 ISSN 0025-1909.
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Official URL: http://dx.doi.org/10.1287/mnsc.1060.0505
Abstract
Quasi-Monte Carlo methods overcome the problem of sample clustering in regular Monte Carlo simulation and have been shown to improve simulation efficiency in the derivatives pricing literature when the price is expressed as a multidimensional integration and the integrand is suitably smooth. For portfolio value-at-risk (VaR) problems, the distribution of portfolio value change is based on the expectation of an indicator function, hence the integrand is discontinuous. The purpose of this paper is to smooth the expectation estimation of an indicator function via Fourier transform so that the faster convergence rate of quasi-Monte Carlo methods can be reclaimed theoretically. Under fairly mild assumptions, the simulation of portfolio value-at-risk is fast and accurate. Numerical examples elucidate the advantage of the proposed approach over regular Monte Carlo and quasi-Monte Carlo methods.
Item Type: | Journal Article | ||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management | ||||
Divisions: | Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School |
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Journal or Publication Title: | Management Science | ||||
Publisher: | Institute for Operations Research and the Management Sciences (I N F O R M S) | ||||
ISSN: | 0025-1909 | ||||
Official Date: | June 2006 | ||||
Dates: |
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Volume: | Vol.52 | ||||
Number: | No.6 | ||||
Number of Pages: | 14 | ||||
Page Range: | pp. 925-938 | ||||
DOI: | 10.1287/mnsc.1060.0505 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Funder: | University of Singapore | ||||
Grant number: | R-146-000-045-101, R-146-000-059-112 |
Data sourced from Thomson Reuters' Web of Knowledge
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