AAD and least-square Monte Carlo: Fast Bermudan-style options and XVA Greeks

Luca Capriotti, Yupeng Jiang and Andrea Macrina ()
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Luca Capriotti: Quantitative Strategies, Global Markets, Credit Suisse Group, One Cabot Square and Department of Mathematics, University College London, Postal: London, UK
Yupeng Jiang: Department of Mathematics, University College London, Postal: London, UK
Andrea Macrina: Department of Mathematics, University College London and Department of Actuarial Science, University of Cape Town, Postal: London, UK and Rondebosch, South Africa

Algorithmic Finance, 2017, vol. 6, issue 1-2, 35-49

Abstract: We show how Adjoint Algorithmic Differentiation (AAD) can be used to calculate price sensitivities in regression-based Monte Carlo methods reliably and orders of magnitude faster than with standard finite-difference approaches. We present the AAD version of the celebrated least-square algorithms of Tsitsiklis and Van Roy (2001) and Longstaff and Schwartz (2001) . By discussing in detail examples of practical relevance, we demonstrate how accounting for the contributions associated with the regression functions is crucial to obtain accurate estimates of the Greeks, especially in XVA applications.

Keywords: Adjoint algorithmic differentiation (AAD); Monte Carlo methods; Bermudan-style options; valuation adjustments (XVA) (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0057

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