{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:53Z","timestamp":1750219733706,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,24]]},"DOI":"10.1145\/3607720.3607736","type":"proceedings-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T17:14:08Z","timestamp":1699895648000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Current Trends in AI-Based Derivatives Pricing: A Review"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6324-8348","authenticated-orcid":false,"given":"Mohammed","family":"Ahnouch","sequence":"first","affiliation":[{"name":"Data &amp; Intelligent systems Team, FSTT, Abdelmalek Essadi University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8437-5800","authenticated-orcid":false,"given":"Lotfi","family":"Elaachak","sequence":"additional","affiliation":[{"name":"Data &amp; Intelligent systems Team, FSTT, Abdelmalek Essadi University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2879-4204","authenticated-orcid":false,"given":"Abderrahim","family":"Ghadi","sequence":"additional","affiliation":[{"name":"Data &amp; Intelligent systems Team, FSTT, Abdelmalek Essadi University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1086\/260062"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Heston SL.1993. A closed-form solution for options with stochastic volatility with applications to bond and currency options. The review of financial studies.  Heston SL.1993. A closed-form solution for options with stochastic volatility with applications to bond and currency options. The review of financial studies.","DOI":"10.1093\/rfs\/6.2.327"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1949.10483310"},{"key":"e_1_3_2_1_4_1","article-title":"Monte Carlo methods for security pricing","author":"Boyle P","year":"1997","unstructured":"Boyle P , Broadie M , and Glasserman P. 1997 . Monte Carlo methods for security pricing . Journal of economic dynamics and\u00a0. Boyle P, Broadie M, and Glasserman P.1997. Monte Carlo methods for security pricing. Journal of economic dynamics and\u00a0.","journal-title":"Journal of economic dynamics and\u00a0."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Longstaff FA and Schwartz ES.2001. Valuing American options by simulation: a simple least-squares approach. The review of financial studies.  Longstaff FA and Schwartz ES.2001. Valuing American options by simulation: a simple least-squares approach. The review of financial studies.","DOI":"10.1093\/rfs\/14.1.113"},{"key":"e_1_3_2_1_6_1","unstructured":"Dupire B.1994. Pricing with a smile. Risk.  Dupire B.1994. Pricing with a smile. Risk."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Heston SL.1993. A closed-form solution for options with stochastic volatility with applications to bond and currency options. The review of financial studies.  Heston SL.1993. A closed-form solution for options with stochastic volatility with applications to bond and currency options. The review of financial studies.","DOI":"10.1093\/rfs\/6.2.327"},{"key":"e_1_3_2_1_8_1","unstructured":"Hagan PS Kumar D and Lesniewski AS.2002. Managing smile risk. The Best of\u00a0.  Hagan PS Kumar D and Lesniewski AS.2002. Managing smile risk. The Best of\u00a0."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(76)90022-2"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Duffie D and Kan R.1996. A yield\u2010factor model of interest rates. Mathematical finance.  Duffie D and Kan R.1996. A yield\u2010factor model of interest rates. Mathematical finance.","DOI":"10.1111\/j.1467-9965.1996.tb00123.x"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Brace A Atarek DG and Musiela M.1997. The market model of interest rate dynamics. Mathematical finance.  Brace A Atarek DG and Musiela M.1997. The market model of interest rate dynamics. Mathematical finance.","DOI":"10.1111\/1467-9965.00028"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2951677"},{"volume-title":"Modern Pricing of Interest-Rate Derivatives","author":"Rebonato R.","key":"e_1_3_2_1_13_1","unstructured":"Rebonato R. 2012 Modern pricing of interest-rate derivatives . In Modern Pricing of Interest-Rate Derivatives , Princeton University Press . Rebonato R. 2012 Modern pricing of interest-rate derivatives. In Modern Pricing of Interest-Rate Derivatives, Princeton University Press."},{"volume-title":"SABR model. Encyclopedia of Quantitative Finance","author":"Henry\u2010labord\u00e8re P.","key":"e_1_3_2_1_14_1","unstructured":"Henry\u2010labord\u00e8re P. 2010. SABR model. Encyclopedia of Quantitative Finance . Henry\u2010labord\u00e8re P.2010. SABR model. Encyclopedia of Quantitative Finance."},{"key":"e_1_3_2_1_15_1","article-title":"Calibration and Monte Carlo pricing of the SABR-Hull-White model for long-maturity equity derivatives","author":"Chen B","year":"2011","unstructured":"Chen B , Grzelak LA , and Oosterlee CW. 2011 . Calibration and Monte Carlo pricing of the SABR-Hull-White model for long-maturity equity derivatives . The Journal of Computational\u00a0. Chen B, Grzelak LA, and Oosterlee CW.2011. Calibration and Monte Carlo pricing of the SABR-Hull-White model for long-maturity equity derivatives. The Journal of Computational\u00a0.","journal-title":"The Journal of Computational\u00a0."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.21314\/JCF.2012.254"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Fukasawa M and Gatheral J.2021. A rough SABR formula. arXiv preprint arXiv:2105.05359.  Fukasawa M and Gatheral J.2021. A rough SABR formula. arXiv preprint arXiv:2105.05359.","DOI":"10.2139\/ssrn.3844278"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.21314\/JCF.2016.320"},{"key":"e_1_3_2_1_19_1","unstructured":"Drage D Munro A and Sleeman C.2005. An update on Eurokiwi and Uridashi bonds. Reserve Bank of New Zealand\u00a0.  Drage D Munro A and Sleeman C.2005. An update on Eurokiwi and Uridashi bonds. Reserve Bank of New Zealand\u00a0."},{"key":"e_1_3_2_1_20_1","unstructured":"Xiao T.2021. Autocallable Note.  Xiao T.2021. Autocallable Note."},{"key":"e_1_3_2_1_21_1","unstructured":"Cameron M.2013. Dashing the uridashi dream. Risk.  Cameron M.2013. Dashing the uridashi dream. Risk."},{"key":"e_1_3_2_1_22_1","unstructured":"Cameron M.2013. Uridashi Losses Put at 500 Million Dollars after Nikkei Rebounds. Risk. Net.  Cameron M.2013. Uridashi Losses Put at 500 Million Dollars after Nikkei Rebounds. Risk. Net."},{"key":"e_1_3_2_1_23_1","unstructured":"Cont R Deguest R and He XD.2013. Loss-based risk measures. Statistics & Risk Modeling. 10.1524\/strm.2013.1132\/html.  Cont R Deguest R and He XD.2013. Loss-based risk measures. Statistics & Risk Modeling. 10.1524\/strm.2013.1132\/html."},{"key":"e_1_3_2_1_24_1","unstructured":"Col AD and Kuppinger P.2017. The interplay between stochastic volatility and correlations in equity autocallables. Available at SSRN 3228065.  Col AD and Kuppinger P.2017. The interplay between stochastic volatility and correlations in equity autocallables. Available at SSRN 3228065."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.21314\/JOP.2014.144"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Lockwood E.2015. Predicting the unpredictable: Value-at-risk performativity and the politics of financial uncertainty. Review of international political economy. 22 4 719-756.  Lockwood E.2015. Predicting the unpredictable: Value-at-risk performativity and the politics of financial uncertainty. Review of international political economy. 22 4 719-756.","DOI":"10.1080\/09692290.2014.957233"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Zeissler AG and Metrick A.2014. JPMorgan Chase London Whale C: Risk Limits Metrics and Models. Yale Program on Financial Stability Case Study.  Zeissler AG and Metrick A.2014. JPMorgan Chase London Whale C: Risk Limits Metrics and Models. Yale Program on Financial Stability Case Study.","DOI":"10.2139\/ssrn.2577856"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Malliaris M and Salchenberger L.1993. A neural network model for estimating option prices. Applied Intelligence.  Malliaris M and Salchenberger L.1993. A neural network model for estimating option prices. Applied Intelligence.","DOI":"10.1007\/BF00871937"},{"key":"e_1_3_2_1_29_1","unstructured":"Ren Y Madan D and Qian MQ.2007. [C] Calibrating and pricing with embedded local volatility models. RISK-LONDON-RISK\u00a0.  Ren Y Madan D and Qian MQ.2007. [C] Calibrating and pricing with embedded local volatility models. RISK-LONDON-RISK\u00a0."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Cuchiero C Khosrawi W and Teichmann J.2020. A generative adversarial network approach to calibration of local stochastic volatility models. Risks.  Cuchiero C Khosrawi W and Teichmann J.2020. A generative adversarial network approach to calibration of local stochastic volatility models. Risks.","DOI":"10.3390\/risks8040101"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Guyon J and Lekeufack J.2022. Volatility is (mostly) path-dependent. Volatility Is (Mostly) Path-Dependent (July\u00a0.  Guyon J and Lekeufack J.2022. Volatility is (mostly) path-dependent. Volatility Is (Mostly) Path-Dependent (July\u00a0.","DOI":"10.2139\/ssrn.4174589"},{"key":"e_1_3_2_1_32_1","unstructured":"Bayer C Horvath B Muguruza A and Stemper B.2019. On deep calibration of (rough) stochastic volatility models. arXiv preprint arXiv\u00a0.  Bayer C Horvath B Muguruza A and Stemper B.2019. On deep calibration of (rough) stochastic volatility models. arXiv preprint arXiv\u00a0."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Horvath B Teichmann J and \u017duri\u010d \u017d.2021. Deep hedging under rough volatility. Risks.  Horvath B Teichmann J and \u017duri\u010d \u017d.2021. Deep hedging under rough volatility. Risks.","DOI":"10.2139\/ssrn.3778043"},{"key":"e_1_3_2_1_34_1","unstructured":"William Karlsson Lille DANIELSAPHIR.2021. Value at Risk Estimation with Neural Networks.  William Karlsson Lille DANIELSAPHIR.2021. Value at Risk Estimation with Neural Networks."},{"key":"e_1_3_2_1_35_1","article-title":"Machine learning in risk measurement: Gaussian process regression for value-at-risk and expected shortfall","author":"Wilkens S.","year":"2019","unstructured":"Wilkens S. 2019 . Machine learning in risk measurement: Gaussian process regression for value-at-risk and expected shortfall . Journal of Risk Management in Financial\u00a0. Wilkens S.2019. Machine learning in risk measurement: Gaussian process regression for value-at-risk and expected shortfall. Journal of Risk Management in Financial\u00a0.","journal-title":"Journal of Risk Management in Financial\u00a0."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Vert JP Tsuda K and Sch\u00f6lkopf B.2004. A primer on kernel methods. Kernel methods in computational\u00a0.  Vert JP Tsuda K and Sch\u00f6lkopf B.2004. A primer on kernel methods. Kernel methods in computational\u00a0.","DOI":"10.7551\/mitpress\/4057.001.0001"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Huge B and Savine A.2020. Differential machine learning. arXiv preprint arXiv:2005.02347.  Huge B and Savine A.2020. Differential machine learning. arXiv preprint arXiv:2005.02347.","DOI":"10.2139\/ssrn.3591734"},{"key":"e_1_3_2_1_38_1","unstructured":"Aslaksen EA.2021. Differential Deep Learning for Pricing Exotic Financial Derivatives.  Aslaksen EA.2021. Differential Deep Learning for Pricing Exotic Financial Derivatives."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2013.05.280"},{"volume-title":"Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings 4, 1-1.","author":"Yang Q.","key":"e_1_3_2_1_40_1","unstructured":"Yang Q. 2008. An introduction to transfer learning . Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings 4, 1-1. Yang Q.2008. An introduction to transfer learning. Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings 4, 1-1."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Giurca A and Borovkova S.2021. Delta hedging of derivatives using deep reinforcement learning. Available at SSRN 3847272.  Giurca A and Borovkova S.2021. Delta hedging of derivatives using deep reinforcement learning. Available at SSRN 3847272.","DOI":"10.2139\/ssrn.3847272"},{"volume-title":"2018 Reinforcement learning: An introduction","author":"Sutton RS","key":"e_1_3_2_1_42_1","unstructured":"Sutton RS and Barto AG . 2018 Reinforcement learning: An introduction . MIT press . Sutton RS and Barto AG. 2018 Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_1_43_1","unstructured":"Sorokin I Seleznev A Pavlov M Fedorov A and Ignateva A.2015. Deep attention recurrent Q-network. arXiv preprint arXiv:1512.01693.  Sorokin I Seleznev A Pavlov M Fedorov A and Ignateva A.2015. Deep attention recurrent Q-network. arXiv preprint arXiv:1512.01693."},{"key":"e_1_3_2_1_44_1","unstructured":"Antoniou A Storkey A and Edwards H.2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340.  Antoniou A Storkey A and Edwards H.2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Buehler H Horvath B Lyons T Arribas IP and Wood B.2020. A data-driven market simulator for small data environments. arXiv preprint arXiv:2006.14498.  Buehler H Horvath B Lyons T Arribas IP and Wood B.2020. A data-driven market simulator for small data environments. arXiv preprint arXiv:2006.14498.","DOI":"10.2139\/ssrn.3632431"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Henry-Labordere P.2019. Generative models for financial data. Available at SSRN 3408007.  Henry-Labordere P.2019. Generative models for financial data. Available at SSRN 3408007.","DOI":"10.2139\/ssrn.3408007"},{"key":"e_1_3_2_1_47_1","unstructured":"Vaserstein LN.1969. Markov processes over denumerable products of spaces describing large systems of automata. Problemy Peredachi Informatsii.  Vaserstein LN.1969. Markov processes over denumerable products of spaces describing large systems of automata. Problemy Peredachi Informatsii."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Lokeshwar V Bharadwaj V and Jain S.2022. Explainable neural network for pricing and universal static hedging of contingent claims. Applied Mathematics and\u00a0.  Lokeshwar V Bharadwaj V and Jain S.2022. Explainable neural network for pricing and universal static hedging of contingent claims. Applied Mathematics and\u00a0.","DOI":"10.1016\/j.amc.2021.126775"},{"key":"e_1_3_2_1_49_1","unstructured":"Arrieta AB D\u00edaz-Rodr\u00edguez N Ser JD and Bennetot A.2020. Explainable Artificial Intelligence (XAI): Concepts taxonomies opportunities and challenges toward responsible AI. Information fusion.  Arrieta AB D\u00edaz-Rodr\u00edguez N Ser JD and Bennetot A.2020. Explainable Artificial Intelligence (XAI): Concepts taxonomies opportunities and challenges toward responsible AI. Information fusion."},{"key":"e_1_3_2_1_50_1","unstructured":"Mundhenk TN Chen BY and Friedland G.2019. Efficient saliency maps for explainable AI. arXiv preprint arXiv:1911.11293.  Mundhenk TN Chen BY and Friedland G.2019. Efficient saliency maps for explainable AI. arXiv preprint arXiv:1911.11293."},{"key":"e_1_3_2_1_51_1","unstructured":"Atrey A Clary K and Jensen D.2019. Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning. arXiv preprint arXiv:1912.05743.  Atrey A Clary K and Jensen D.2019. Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning. arXiv preprint arXiv:1912.05743."},{"volume-title":"2002 Learning with kernels: support vector machines, regularization, optimization, and beyond","author":"Sch\u00f6lkopf B","key":"e_1_3_2_1_52_1","unstructured":"Sch\u00f6lkopf B , Smola AJ , and Bach F . 2002 Learning with kernels: support vector machines, regularization, optimization, and beyond . MIT press . Sch\u00f6lkopf B, Smola AJ, and Bach F. 2002 Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press."},{"key":"e_1_3_2_1_53_1","unstructured":"Zhao H and Tsai YHH.2019. Learning neural networks with adaptive regularization. Advances in Neural\u00a0.  Zhao H and Tsai YHH.2019. Learning neural networks with adaptive regularization. Advances in Neural\u00a0."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Weng B Lu L Wang X and Megahed FM.2018. Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with\u00a0.  Weng B Lu L Wang X and Megahed FM.2018. Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with\u00a0.","DOI":"10.1016\/j.eswa.2018.06.016"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Wang M Zhang Y Qin C Liu P and Zhang Q.2022. Option Pricing Model Combining Ensemble Learning Methods and Network Learning Structure. Mathematical Problems in\u00a0.  Wang M Zhang Y Qin C Liu P and Zhang Q.2022. Option Pricing Model Combining Ensemble Learning Methods and Network Learning Structure. Mathematical Problems in\u00a0.","DOI":"10.1155\/2022\/2590940"},{"key":"e_1_3_2_1_56_1","unstructured":"Eba.2021. EBA Discussion Paper on Machine Learning for IRB Models. 29.  Eba.2021. EBA Discussion Paper on Machine Learning for IRB Models. 29."},{"key":"e_1_3_2_1_57_1","unstructured":"Eba.2018. EBA Report on the Prudential Risks and Opportunities arising for Institutions from Fintech. 56.  Eba.2018. EBA Report on the Prudential Risks and Opportunities arising for Institutions from Fintech. 56."}],"event":{"name":"NISS 2023: The 6th International Conference on Networking, Intelligent Systems & Security","acronym":"NISS 2023","location":"Larache Morocco"},"container-title":["Proceedings of the 6th International Conference on Networking, Intelligent Systems &amp; Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3607720.3607736","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3607720.3607736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:29Z","timestamp":1750178189000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3607720.3607736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,24]]},"references-count":57,"alternative-id":["10.1145\/3607720.3607736","10.1145\/3607720"],"URL":"https:\/\/doi.org\/10.1145\/3607720.3607736","relation":{},"subject":[],"published":{"date-parts":[[2023,5,24]]},"assertion":[{"value":"2023-11-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}