{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:09Z","timestamp":1750220169579,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/T517963\/1, EP\/V00784X\/1"],"award-info":[{"award-number":["EP\/T517963\/1, EP\/V00784X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,2]]},"DOI":"10.1145\/3533271.3561740","type":"proceedings-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T22:20:22Z","timestamp":1666304422000},"page":"96-104","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Model-Agnostic Pricing of Exotic Derivatives Using Signatures"],"prefix":"10.1145","author":[{"given":"Andrew","family":"Alden","sequence":"first","affiliation":[{"name":"King's College London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmine","family":"Ventre","sequence":"additional","affiliation":[{"name":"King's College London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Blanka","family":"Horvath","sequence":"additional","affiliation":[{"name":"University of Oxford, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gordon","family":"Lee","sequence":"additional","affiliation":[{"name":"Independent Consultant, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Afshine Amidi and Shervine Amidi. [n.d.]. A detailed example of how to generate your data in parallel with PyTorch. https:\/\/stanford.edu\/\u00a0shervine\/blog\/pytorch-how-to-generate-data-parallel  Afshine Amidi and Shervine Amidi. [n.d.]. A detailed example of how to generate your data in parallel with PyTorch. https:\/\/stanford.edu\/\u00a0shervine\/blog\/pytorch-how-to-generate-data-parallel"},{"key":"e_1_3_2_1_2_1","unstructured":"Imanol\u00a0Perez Arribas. 2018. Derivative Pricing Using Signature Payoffs. (2018). arXiv:1809.09466  Imanol\u00a0Perez Arribas. 2018. Derivative Pricing Using Signature Payoffs. (2018). arXiv:1809.09466"},{"key":"e_1_3_2_1_3_1","volume-title":"arXiv:1607.06450","author":"Ba Jimmy\u00a0Lei","year":"2016","unstructured":"Jimmy\u00a0Lei Ba , Jamie\u00a0Ryan Kiros , and Geoffrey\u00a0 E. Hinton . 2016. Layer Normalization . ( 2016 ). arXiv:1607.06450 Jimmy\u00a0Lei Ba, Jamie\u00a0Ryan Kiros, and Geoffrey\u00a0E. Hinton. 2016. Layer Normalization. (2016). arXiv:1607.06450"},{"key":"e_1_3_2_1_4_1","unstructured":"Daniel Bartl Mathias Beiglb\u00f6ck and Pammer Gudmund. 2021. The Wasserstein Space of Stochastic Processes. (2021). arXiv:2104.14245  Daniel Bartl Mathias Beiglb\u00f6ck and Pammer Gudmund. 2021. The Wasserstein Space of Stochastic Processes. (2021). arXiv:2104.14245"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1086\/260062"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Hans B\u00fchler Blanka Horvath Terry Lyons Imanol\u00a0Perez Arribas and Ben Wood. 2020. A Data-Driven Market Simulator for Small Data Environments. (2020). arXiv:2006.14498  Hans B\u00fchler Blanka Horvath Terry Lyons Imanol\u00a0Perez Arribas and Ben Wood. 2020. A Data-Driven Market Simulator for Small Data Environments. (2020). arXiv:2006.14498","DOI":"10.2139\/ssrn.3632431"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031689014"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.4236\/jmf.2016.64043"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.2307\/1911242"},{"volume-title":"Monte Carlo Methods in Financial Engineering","author":"Glasserman Paul","key":"e_1_3_2_1_11_1","unstructured":"Paul Glasserman . 2004. Monte Carlo Methods in Financial Engineering . Springer , New York, NY . https:\/\/doi.org\/10.1007\/978-0-387-21617-1 10.1007\/978-0-387-21617-1 Paul Glasserman. 2004. Monte Carlo Methods in Financial Engineering. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-0-387-21617-1"},{"volume-title":"Deep Learning","author":"Goodfellow Ian","key":"e_1_3_2_1_12_1","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press . http:\/\/www.deeplearningbook.org Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org"},{"key":"e_1_3_2_1_13_1","first-page":"723","article-title":"A Kernel Two-Sample Test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton , Karsten\u00a0 M. Borgwardt , Malte\u00a0 J. Rasch , Bernhard Sch\u00f6lkopf , and Alexander Smola . 2012 . A Kernel Two-Sample Test . Journal Of Machine Learning Research 13 (2012), 723 \u2013 773 . Arthur Gretton, Karsten\u00a0M. Borgwardt, Malte\u00a0J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A Kernel Two-Sample Test. Journal Of Machine Learning Research 13 (2012), 723\u2013773.","journal-title":"Journal Of Machine Learning Research"},{"volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie Trevor","key":"e_1_3_2_1_14_1","unstructured":"Trevor Hastie , Robert Tibshirani , and Jerome Friedman . 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , Second Edition (2nded.). Springer New York , NY. https:\/\/doi.org\/10.1007\/978-0-387-84858-7 10.1007\/978-0-387-84858-7 Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2nded.). Springer New York, NY. https:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"e_1_3_2_1_15_1","unstructured":"Calypso Herrera Florian Krach Pierre Ruyssen and Josef Teichmann. 2021. Optimal Stopping via Randomized Neural Networks. (2021). arXiv:2104.13669  Calypso Herrera Florian Krach Pierre Ruyssen and Josef Teichmann. 2021. Optimal Stopping via Randomized Neural Networks. (2021). arXiv:2104.13669"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/6.2.327"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Blanka Horvath Aitor Muguruza and Mehdi Tomas. 2019. Deep Learning Volatility. (2019). arXiv:1901.09647  Blanka Horvath Aitor Muguruza and Mehdi Tomas. 2019. Deep Learning Volatility. (2019). arXiv:1901.09647","DOI":"10.2139\/ssrn.3322085"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Brian Huge and Antoine Savine. 2020. Differential Machine Learning. (2020). arXiv:2005.02347  Brian Huge and Antoine Savine. 2020. Differential Machine Learning. (2020). arXiv:2005.02347","DOI":"10.2139\/ssrn.3591734"},{"volume-title":"Monte Carlo methods in finance","author":"J\u00e4ckel Peter","key":"e_1_3_2_1_19_1","unstructured":"Peter J\u00e4ckel . 2002. Monte Carlo methods in finance . John Wiley & Sons, Ltd. , Chichester, England. Peter J\u00e4ckel. 2002. Monte Carlo methods in finance. John Wiley & Sons, Ltd., Chichester, England."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Artificial Intelligence and Statistics, AISTATS 2018","author":"Law Chung\u00a0Leon","year":"2018","unstructured":"Ho\u00a0 Chung\u00a0Leon Law , Danica\u00a0 J. Sutherland , Dino Sejdinovic , and Seth Flaxman . 2018 . Bayesian Approaches to Distribution Regression . In International Conference on Artificial Intelligence and Statistics, AISTATS 2018 , 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain(Proceedings of Machine Learning Research, Vol.\u00a084). PMLR, 1167\u20131176. http:\/\/proceedings.mlr.press\/v84\/law18a.html Ho\u00a0Chung\u00a0Leon Law, Danica\u00a0J. Sutherland, Dino Sejdinovic, and Seth Flaxman. 2018. Bayesian Approaches to Distribution Regression. In International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain(Proceedings of Machine Learning Research, Vol.\u00a084). PMLR, 1167\u20131176. http:\/\/proceedings.mlr.press\/v84\/law18a.html"},{"key":"#cr-split#-e_1_3_2_1_21_1.1","doi-asserted-by":"crossref","unstructured":"Gordon Lee J\u00f6erg Kienitz Nikolai Nowaczyk and Qingxin Geng. 2021. Dynamically Controlled Kernel Estimation. (2021). https:\/\/doi.org\/10.2139\/ssrn.3829701 10.2139\/ssrn.3829701","DOI":"10.2139\/ssrn.3829701"},{"key":"#cr-split#-e_1_3_2_1_21_1.2","doi-asserted-by":"crossref","unstructured":"Gordon Lee J\u00f6erg Kienitz Nikolai Nowaczyk and Qingxin Geng. 2021. Dynamically Controlled Kernel Estimation. (2021). https:\/\/doi.org\/10.2139\/ssrn.3829701","DOI":"10.2139\/ssrn.3829701"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Lemercier Maud","year":"2021","unstructured":"Maud Lemercier , Cristopher Salvi , Theodoros Damoulas , Edwin\u00a0 V. Bonilla , and Terry Lyons . 2021 . Distribution Regression for Sequential Data . In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) ( San Diego, California, USA). PMLR, 3754\u20133762. Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin\u00a0V. Bonilla, and Terry Lyons. 2021. Distribution Regression for Sequential Data. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) (San Diego, California, USA). PMLR, 3754\u20133762."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455334"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/risks7010016"},{"key":"e_1_3_2_1_25_1","volume-title":"Decoupled Weight Decay Regularization. In 7th International Conference on Learning Representations","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter . 2019 . Decoupled Weight Decay Regularization. In 7th International Conference on Learning Representations ( New Orleans, LA, USA) (ICLR 2019). Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In 7th International Conference on Learning Representations (New Orleans, LA, USA) (ICLR 2019)."},{"key":"e_1_3_2_1_26_1","unstructured":"Terry Lyons Sina Nejad and Imanol\u00a0Perez Arribas. 2019. Nonparametric pricing and hedging of exotic derivatives. (2019). arXiv:1905.00711  Terry Lyons Sina Nejad and Imanol\u00a0Perez Arribas. 2019. Nonparametric pricing and hedging of exotic derivatives. (2019). arXiv:1905.00711"},{"key":"e_1_3_2_1_27_1","volume-title":"Chebyshev Greeks: Smoothing Gamma without Bias.","author":"Maran Andrea","year":"2021","unstructured":"Andrea Maran , Andrea Pallavicini , and Stefano Scoleri . 2021 . Chebyshev Greeks: Smoothing Gamma without Bias. (2021). https:\/\/doi.org\/10.2139\/ssrn.3872744 10.2139\/ssrn.3872744 Andrea Maran, Andrea Pallavicini, and Stefano Scoleri. 2021. Chebyshev Greeks: Smoothing Gamma without Bias. (2021). https:\/\/doi.org\/10.2139\/ssrn.3872744"},{"key":"e_1_3_2_1_28_1","unstructured":"Johannes Ruf and Weiguan Wang. 2020. Neural networks for option pricing and hedging: a literature review. (2020). arXiv:1911.05620  Johannes Ruf and Weiguan Wang. 2020. Neural networks for option pricing and hedging: a literature review. (2020). arXiv:1911.05620"},{"key":"e_1_3_2_1_29_1","unstructured":"Cristopher Salvi Thomas Cass James Foster Terry Lyons and Weixin Yang. 2020. The Signature Kernel is the Solution of a Goursat PDE. (2020). arXiv:2006.14794  Cristopher Salvi Thomas Cass James Foster Terry Lyons and Weixin Yang. 2020. The Signature Kernel is the Solution of a Goursat PDE. (2020). arXiv:2006.14794"},{"key":"e_1_3_2_1_30_1","volume-title":"35th Conference on Neural Information Processing Systems(NeurIPS","author":"Salvi Cristopher","year":"2021","unstructured":"Cristopher Salvi , Maud Lemercier , Chong Liu , Blanka Horvath , Theodoros Damoulas , and Terry Lyons . 2021 . Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes . In 35th Conference on Neural Information Processing Systems(NeurIPS 2021). Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Horvath, Theodoros Damoulas, and Terry Lyons. 2021. Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes. In 35th Conference on Neural Information Processing Systems(NeurIPS 2021)."},{"volume-title":"Modern Computational Finance: AAD and Parallel Simulations","author":"Savine Antoine","key":"e_1_3_2_1_31_1","unstructured":"Antoine Savine . 2019. Modern Computational Finance: AAD and Parallel Simulations . John Wiley & Sons, Ltd. Antoine Savine. 2019. Modern Computational Finance: AAD and Parallel Simulations. John Wiley & Sons, Ltd."},{"key":"e_1_3_2_1_32_1","volume-title":"Differential equations driven by rough signals. Revista Matem\u00e1tica Iberoamericana 14","author":"Lyons Terry J.","year":"1998","unstructured":"Terry J. Lyons . 1998. Differential equations driven by rough signals. Revista Matem\u00e1tica Iberoamericana 14 ( 1998 ), 215\u2013310. Issue 2. http:\/\/eudml.org\/doc\/39555 Terry J. Lyons. 1998. Differential equations driven by rough signals. Revista Matem\u00e1tica Iberoamericana 14 (1998), 215\u2013310. Issue 2. http:\/\/eudml.org\/doc\/39555"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the International Congress of Mathematicians (Korea).","author":"Lyons Terry J.","year":"2014","unstructured":"Terry J. Lyons . 2014 . Rough paths, signatures and the modelling of functions on streams . In Proceedings of the International Congress of Mathematicians (Korea). Terry J. Lyons. 2014. Rough paths, signatures and the modelling of functions on streams. In Proceedings of the International Congress of Mathematicians (Korea)."},{"volume-title":"Arbitrage Theory in Continuous Time","author":"Bj\u00f6rk Tomas","key":"e_1_3_2_1_34_1","unstructured":"Tomas Bj\u00f6rk . 2004. Arbitrage Theory in Continuous Time . Oxford University Press . https:\/\/doi.org\/DOI:10.1093\/0199271267.001.0001 10.1093\/0199271267.001.0001 Tomas Bj\u00f6rk. 2004. Arbitrage Theory in Continuous Time. Oxford University Press. https:\/\/doi.org\/DOI:10.1093\/0199271267.001.0001"},{"key":"e_1_3_2_1_35_1","volume-title":"Deep Hedging: Learning to Simulate Equity Option Markets.","author":"Wiese Magnus","year":"2019","unstructured":"Magnus Wiese , Lianjun Bai , Ben Wood , and Hans Buehler . 2019 . Deep Hedging: Learning to Simulate Equity Option Markets. (2019). https:\/\/doi.org\/10.2139\/ssrn.3470756 10.2139\/ssrn.3470756 Magnus Wiese, Lianjun Bai, Ben Wood, and Hans Buehler. 2019. Deep Hedging: Learning to Simulate Equity Option Markets. (2019). https:\/\/doi.org\/10.2139\/ssrn.3470756"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Magnus Wiese Ben Wood Alexandre Pachoud Ralf Korn Hans Buehler Phillip Murray and Lianjun Bai. 2021. Multi-Asset Spot and Option Market Simulation. (2021). arXiv:2112.06823  Magnus Wiese Ben Wood Alexandre Pachoud Ralf Korn Hans Buehler Phillip Murray and Lianjun Bai. 2021. Multi-Asset Spot and Option Market Simulation. (2021). arXiv:2112.06823","DOI":"10.2139\/ssrn.3980817"}],"event":{"name":"ICAIF '22: 3rd ACM International Conference on AI in Finance","sponsor":["ACM Association for Computing Machinery"],"location":"New York NY USA","acronym":"ICAIF '22"},"container-title":["Proceedings of the Third ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561740","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3533271.3561740","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:39Z","timestamp":1750186839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561740"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":37,"alternative-id":["10.1145\/3533271.3561740","10.1145\/3533271"],"URL":"https:\/\/doi.org\/10.1145\/3533271.3561740","relation":{},"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"2022-10-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}