Capriotti, L;
Jiang, Y;
Macrina, A;
(2017)
AAD and least-square Monte Carlo: fast Bermudan-style options and XVA Greeks.
Algorithmic Finance
, 6
(1-2)
pp. 35-49.
10.3233/AF-170201.
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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.
| Type: | Article |
|---|---|
| Title: | AAD and least-square Monte Carlo: fast Bermudan-style options and XVA Greeks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/AF-170201 |
| Publisher version: | http://dx.doi.org/10.3233/AF-170201 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Adjoint algorithmic differentiation, Monte Carlo methods, Bermudan-style options, Valuation adjustments (XVA) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1523595 |
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