15 years of Adjoint Algorithmic Differentiation (AAD) in finance
Following the seminal ‘Smoking Adjoint’ paper by Giles and Glasserman [Smoking adjoints: Fast monte carlo greeks. Risk, 2006, <strong>19</strong>, 88–92], the development of Adjoint Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial industry. I...
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| Format: | Journal article |
| Language: | English |
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Taylor & Francis
2024
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| _version_ | 1797113068446023680 |
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| author | Capriotti, L Giles, M |
| author_facet | Capriotti, L Giles, M |
| author_sort | Capriotti, L |
| collection | OXFORD |
| description | Following the seminal ‘Smoking Adjoint’ paper by Giles and Glasserman [Smoking adjoints: Fast monte carlo greeks. Risk, 2006, <strong>19</strong>, 88–92], the development of Adjoint Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial industry. In this paper, we provide a tutorial of this technique, illustrate how it is immediately applicable for Monte Carlo and Partial Differential Equations applications, the two main numerical techniques used for option pricing, and review the most significant literature in quantitative finance of the past fifteen years. |
| first_indexed | 2024-03-07T08:28:14Z |
| format | Journal article |
| id | oxford-uuid:cd9a682f-0d4d-4f7a-b23d-15f20d5d5e51 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:58:26Z |
| publishDate | 2024 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | oxford-uuid:cd9a682f-0d4d-4f7a-b23d-15f20d5d5e512024-03-26T12:18:19Z15 years of Adjoint Algorithmic Differentiation (AAD) in financeJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:cd9a682f-0d4d-4f7a-b23d-15f20d5d5e51EnglishSymplectic ElementsTaylor & Francis2024Capriotti, LGiles, MFollowing the seminal ‘Smoking Adjoint’ paper by Giles and Glasserman [Smoking adjoints: Fast monte carlo greeks. Risk, 2006, <strong>19</strong>, 88–92], the development of Adjoint Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial industry. In this paper, we provide a tutorial of this technique, illustrate how it is immediately applicable for Monte Carlo and Partial Differential Equations applications, the two main numerical techniques used for option pricing, and review the most significant literature in quantitative finance of the past fifteen years. |
| spellingShingle | Capriotti, L Giles, M 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title | 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title_full | 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title_fullStr | 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title_full_unstemmed | 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title_short | 15 years of Adjoint Algorithmic Differentiation (AAD) in finance |
| title_sort | 15 years of adjoint algorithmic differentiation aad in finance |
| work_keys_str_mv | AT capriottil 15yearsofadjointalgorithmicdifferentiationaadinfinance AT gilesm 15yearsofadjointalgorithmicdifferentiationaadinfinance |