Quasi-Monte Carlo for finance applications

Monte Carlo methods are used extensively in computational finance to estimate the price of financial derivative options. We review the use of quasi-Monte Carlo methods to obtain the same accuracy at a much lower computational cost, and focus on three key ingredients: the generation of Sobol' an...

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Autori principali: Giles, M, Kuo, F, Sloan, I, Waterhouse, B
Natura: Journal article
Lingua:English
Pubblicazione: 2008
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author Giles, M
Kuo, F
Sloan, I
Waterhouse, B
author_facet Giles, M
Kuo, F
Sloan, I
Waterhouse, B
author_sort Giles, M
collection OXFORD
description Monte Carlo methods are used extensively in computational finance to estimate the price of financial derivative options. We review the use of quasi-Monte Carlo methods to obtain the same accuracy at a much lower computational cost, and focus on three key ingredients: the generation of Sobol' and lattice points, reduction of effective dimension using the principal component analysis approach at full potential, and randomization by shifting or digital shifting to give an unbiased estimator with a confidence interval. Our aim is to provide a starting point for finance practitioners new to quasi-Monte Carlo methods. © Austral. Mathematical Soc. 2008.
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spelling oxford-uuid:6749b9cf-64c7-4b83-84d4-38b9ba92db252022-03-26T18:37:17ZQuasi-Monte Carlo for finance applicationsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6749b9cf-64c7-4b83-84d4-38b9ba92db25EnglishSymplectic Elements at Oxford2008Giles, MKuo, FSloan, IWaterhouse, BMonte Carlo methods are used extensively in computational finance to estimate the price of financial derivative options. We review the use of quasi-Monte Carlo methods to obtain the same accuracy at a much lower computational cost, and focus on three key ingredients: the generation of Sobol' and lattice points, reduction of effective dimension using the principal component analysis approach at full potential, and randomization by shifting or digital shifting to give an unbiased estimator with a confidence interval. Our aim is to provide a starting point for finance practitioners new to quasi-Monte Carlo methods. © Austral. Mathematical Soc. 2008.
spellingShingle Giles, M
Kuo, F
Sloan, I
Waterhouse, B
Quasi-Monte Carlo for finance applications
title Quasi-Monte Carlo for finance applications
title_full Quasi-Monte Carlo for finance applications
title_fullStr Quasi-Monte Carlo for finance applications
title_full_unstemmed Quasi-Monte Carlo for finance applications
title_short Quasi-Monte Carlo for finance applications
title_sort quasi monte carlo for finance applications
work_keys_str_mv AT gilesm quasimontecarloforfinanceapplications
AT kuof quasimontecarloforfinanceapplications
AT sloani quasimontecarloforfinanceapplications
AT waterhouseb quasimontecarloforfinanceapplications