The computation of Greeks with multilevel Monte Carlo

<p>In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is com...

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Yazar: Burgos, S
Diğer Yazarlar: Giles, M
Materyal Türü: Tez
Dil:English
Baskı/Yayın Bilgisi: 2014
Konular:
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author Burgos, S
author2 Giles, M
author_facet Giles, M
Burgos, S
author_sort Burgos, S
collection OXFORD
description <p>In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly.</p> <p>Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators?</p> <p>We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms.</p> <p>We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process.</p> <p>We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.</p>
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spelling oxford-uuid:6453a93b-9daf-4bfe-8c77-9cd6802f77dd2022-03-26T18:18:14ZThe computation of Greeks with multilevel Monte CarloThesishttp://purl.org/coar/resource_type/c_db06uuid:6453a93b-9daf-4bfe-8c77-9cd6802f77ddMathematicsNumerical analysisProbability theory and stochastic processesMathematical financeEnglishOxford University Research Archive - Valet2014Burgos, SGiles, M<p>In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly.</p> <p>Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators?</p> <p>We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms.</p> <p>We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process.</p> <p>We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.</p>
spellingShingle Mathematics
Numerical analysis
Probability theory and stochastic processes
Mathematical finance
Burgos, S
The computation of Greeks with multilevel Monte Carlo
title The computation of Greeks with multilevel Monte Carlo
title_full The computation of Greeks with multilevel Monte Carlo
title_fullStr The computation of Greeks with multilevel Monte Carlo
title_full_unstemmed The computation of Greeks with multilevel Monte Carlo
title_short The computation of Greeks with multilevel Monte Carlo
title_sort computation of greeks with multilevel monte carlo
topic Mathematics
Numerical analysis
Probability theory and stochastic processes
Mathematical finance
work_keys_str_mv AT burgoss thecomputationofgreekswithmultilevelmontecarlo
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