{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T05:11:22Z","timestamp":1724389882495},"reference-count":31,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["14206020"],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Operations Research Letters"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1016\/j.orl.2023.05.007","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T15:17:13Z","timestamp":1685373433000},"page":"408-413","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data-driven hedging of stock index options via deep learning"],"prefix":"10.1016","volume":"51","author":[{"given":"Jie","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1761-3458","authenticated-orcid":false,"given":"Lingfei","family":"Li","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.orl.2023.05.007_br0010","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1080\/14697688.2019.1571683","article-title":"Deep hedging","volume":"19","author":"Buehler","year":"2019","journal-title":"Quant. Finance"},{"issue":"9","key":"10.1016\/j.orl.2023.05.007_br0020","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/14697688.2020.1750679","article-title":"A neural network approach to understanding implied volatility movements","volume":"20","author":"Cao","year":"2020","journal-title":"Quant. Finance"},{"key":"10.1016\/j.orl.2023.05.007_br0030","author":"Chung"},{"key":"10.1016\/j.orl.2023.05.007_br0040","series-title":"Stochastic Analysis and Applications","first-page":"197","article-title":"Hedging with options in models with jumps","author":"Cont","year":"2007"},{"issue":"1","key":"10.1016\/j.orl.2023.05.007_br0050","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3905\/jod.2022.1.165","article-title":"Evaluation of deep learning algorithms for quadratic hedging","volume":"30","author":"Dai","year":"2022","journal-title":"J. Deriv."},{"issue":"1","key":"10.1016\/j.orl.2023.05.007_br0060","first-page":"18","article-title":"Pricing with a smile","volume":"7","author":"Dupire","year":"1994","journal-title":"Risk"},{"key":"10.1016\/j.orl.2023.05.007_br0070","series-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"Glorot","year":"2010"},{"key":"10.1016\/j.orl.2023.05.007_br0080","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"issue":"5","key":"10.1016\/j.orl.2023.05.007_br0090","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1093\/rfs\/hhaa009","article-title":"Empirical asset pricing via machine learning","volume":"33","author":"Gu","year":"2020","journal-title":"Rev. Financ. Stud."},{"key":"10.1016\/j.orl.2023.05.007_br0100","first-page":"249","article-title":"Managing smile risk","volume":"1","author":"Hagan","year":"2002","journal-title":"Wilmott"},{"issue":"1","key":"10.1016\/j.orl.2023.05.007_br0110","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1093\/rfs\/hhm071","article-title":"Investor sentiment and option prices","volume":"21","author":"Han","year":"2008","journal-title":"Rev. Financ. Stud."},{"issue":"34","key":"10.1016\/j.orl.2023.05.007_br0120","doi-asserted-by":"crossref","first-page":"8505","DOI":"10.1073\/pnas.1718942115","article-title":"Solving high-dimensional partial differential equations using deep learning","volume":"115","author":"Han","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.orl.2023.05.007_br0130","series-title":"Advances in Neural Information Processing Systems","article-title":"Deep learning approximation for stochastic control problems. Deep reinforcement learning workshop","author":"Han","year":"2016"},{"issue":"2","key":"10.1016\/j.orl.2023.05.007_br0140","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1093\/rfs\/6.2.327","article-title":"A closed-form solution for options with stochastic volatility with applications to bond and currency options","volume":"6","author":"Heston","year":"1993","journal-title":"Rev. Financ. Stud."},{"issue":"8","key":"10.1016\/j.orl.2023.05.007_br0150","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"9","key":"10.1016\/j.orl.2023.05.007_br0160","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1080\/14697688.2020.1741669","article-title":"Deep learning for ranking response surfaces with applications to optimal stopping problems","volume":"20","author":"Hu","year":"2020","journal-title":"Quant. Finance"},{"issue":"2","key":"10.1016\/j.orl.2023.05.007_br0170","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1540-6261.1987.tb02568.x","article-title":"The pricing of options on assets with stochastic volatilities","volume":"42","author":"Hull","year":"1987","journal-title":"J. Finance"},{"key":"10.1016\/j.orl.2023.05.007_br0180","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jbankfin.2017.05.006","article-title":"Optimal delta hedging for options","volume":"82","author":"Hull","year":"2017","journal-title":"J. Bank. Finance"},{"key":"10.1016\/j.orl.2023.05.007_br0190","series-title":"International Conference on Machine Learning","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"issue":"3","key":"10.1016\/j.orl.2023.05.007_br0200","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1287\/opre.1080.0598","article-title":"Dynamic hedging under jump diffusion with transaction costs","volume":"57","author":"Kennedy","year":"2009","journal-title":"Oper. Res."},{"key":"10.1016\/j.orl.2023.05.007_br0210","author":"Kingma"},{"issue":"5","key":"10.1016\/j.orl.2023.05.007_br0220","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1080\/14697688.2023.2186257","article-title":"A data-driven deep learning approach for options market making","volume":"23","author":"Lai","year":"2023","journal-title":"Quant. Finance"},{"key":"10.1016\/j.orl.2023.05.007_br0230","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jedc.2016.11.001","article-title":"Pure jump models for pricing and hedging VIX derivatives","volume":"74","author":"Li","year":"2017","journal-title":"J. Econ. Dyn. Control"},{"issue":"7","key":"10.1016\/j.orl.2023.05.007_br0240","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1080\/14697688.2017.1413245","article-title":"Learning minimum variance discrete hedging directly from the market","volume":"18","author":"Nian","year":"2018","journal-title":"Quant. Finance"},{"key":"10.1016\/j.orl.2023.05.007_br0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbankfin.2021.106277","article-title":"Learning sequential option hedging models from market data","volume":"133","author":"Nian","year":"2021","journal-title":"J. Bank. Finance"},{"issue":"4","key":"10.1016\/j.orl.2023.05.007_br0260","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1080\/07350015.2021.1931241","article-title":"Hedging with linear regressions and neural networks","volume":"40","author":"Ruf","year":"2022","journal-title":"J. Bus. Econ. Stat."},{"issue":"2","key":"10.1016\/j.orl.2023.05.007_br0270","first-page":"313","article-title":"Deep learning for mortgage risk","volume":"19","author":"Sadhwani","year":"2021","journal-title":"J. Financ. Econom."},{"key":"10.1016\/j.orl.2023.05.007_br0280","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.jcp.2018.08.029","article-title":"DGM: a deep learning algorithm for solving partial differential equations","volume":"375","author":"Sirignano","year":"2018","journal-title":"J. Comput. Phys."},{"issue":"4","key":"10.1016\/j.orl.2023.05.007_br0290","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1080\/14697688.2018.1546053","article-title":"Deep learning for limit order books","volume":"19","author":"Sirignano","year":"2019","journal-title":"Quant. Finance"},{"issue":"4","key":"10.1016\/j.orl.2023.05.007_br0300","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s40304-017-0117-6","article-title":"Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations","volume":"5","author":"Weinan","year":"2017","journal-title":"Commun. Math. Stat."},{"issue":"1","key":"10.1016\/j.orl.2023.05.007_br0310","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/14697688.2022.2135454","article-title":"A two-step framework for arbitrage-free prediction of the implied volatility surface","volume":"23","author":"Zhang","year":"2023","journal-title":"Quant. Finance"}],"container-title":["Operations Research Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167637723000718?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167637723000718?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T10:42:53Z","timestamp":1711968173000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167637723000718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["S0167637723000718"],"URL":"https:\/\/doi.org\/10.1016\/j.orl.2023.05.007","relation":{},"ISSN":["0167-6377"],"issn-type":[{"value":"0167-6377","type":"print"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Data-driven hedging of stock index options via deep learning","name":"articletitle","label":"Article Title"},{"value":"Operations Research Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.orl.2023.05.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}