{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:27Z","timestamp":1755907707650,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T00:00:00Z","timestamp":1700870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21H03493, 20K20492"],"award-info":[{"award-number":["21H03493, 20K20492"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,27]]},"DOI":"10.1145\/3604237.3626870","type":"proceedings-article","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T18:09:47Z","timestamp":1700935787000},"page":"418-426","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9135-2906","authenticated-orcid":false,"given":"Xin","family":"Du","sequence":"first","affiliation":[{"name":"Tanaka-Ishii Laboratory, Waseda University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0530-2082","authenticated-orcid":false,"given":"Kai","family":"Moriyama","sequence":"additional","affiliation":[{"name":"Tanaka-Ishii Laboratory, Waseda University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1752-3951","authenticated-orcid":false,"given":"Kumiko","family":"Tanaka-Ishii","sequence":"additional","affiliation":[{"name":"Tanaka-Ishii Laboratory, Waseda University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00326"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Torben\u00a0G Andersen Tim Bollerslev Francis\u00a0X Diebold and Paul Labys. 2000. Exchange rate returns standardized by realized volatility are (nearly) Gaussian.","DOI":"10.3386\/w7488"},{"key":"e_1_3_2_1_3_1","volume-title":"Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics 37, 6","author":"Baum E","year":"1966","unstructured":"Leonard\u00a0E Baum and Ted Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics 37, 6 (1966), 1554\u20131563."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1964.tb00553.x"},{"volume-title":"Inference in hidden Markov models","author":"Capp\u00e9 Olivier","key":"e_1_3_2_1_5_1","unstructured":"Olivier Capp\u00e9, Eric Moulines, and Tobias Ryd\u00e9n. 2005. Inference in hidden Markov models. Springer, New York ; London. OCLC: ocm61260826."},{"key":"e_1_3_2_1_6_1","volume-title":"Neural ordinary differential equations. Advances in neural information processing systems 31","author":"Chen TQ","year":"2018","unstructured":"Ricky\u00a0TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David\u00a0K Duvenaud. 2018. Neural ordinary differential equations. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"A subordinated stochastic process model with finite variance for speculative prices. Econometrica: journal of the Econometric Society","author":"Clark K","year":"1973","unstructured":"Peter\u00a0K Clark. 1973. A subordinated stochastic process model with finite variance for speculative prices. Econometrica: journal of the Econometric Society (1973), 135\u2013155."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbp001"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2012.08.015"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474930701853616"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_3_2_1_12_1","first-page":"7805","article-title":"Modeling continuous stochastic processes with dynamic normalizing flows","volume":"33","author":"Deng Ruizhi","year":"2020","unstructured":"Ruizhi Deng, Bo Chang, Marcus\u00a0A Brubaker, Greg Mori, and Andreas Lehrmann. 2020. Modeling continuous stochastic processes with dynamic normalizing flows. Advances in Neural Information Processing Systems 33 (2020), 7805\u20137815.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_13_1","volume-title":"NICE: Non-linear Independent Components Estimation. In 3rd International Conference on Learning Representations, ICLR","author":"Dinh Laurent","year":"2015","unstructured":"Laurent Dinh, David Krueger, and Yoshua Bengio. 2015. NICE: Non-linear Independent Components Estimation. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1410.8516"},{"key":"e_1_3_2_1_14_1","volume-title":"Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society","author":"Engle F","year":"1982","unstructured":"Robert\u00a0F Engle. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society (1982), 987\u20131007."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1086\/294743"},{"key":"e_1_3_2_1_16_1","first-page":"109","article-title":"Expansion of \u201cstudent\u2019s\u201d integral in powers of n\u2212 1","volume":"5","author":"Fisher A","year":"1926","unstructured":"Ronald\u00a0A Fisher. 1926. Expansion of \u201cstudent\u2019s\u201d integral in powers of n\u2212 1. Metron 5 (1926), 109\u2013112.","journal-title":"Metron"},{"key":"e_1_3_2_1_17_1","volume-title":"A theory of power-law distributions in financial market fluctuations. Nature 423, 6937","author":"Gabaix Xavier","year":"2003","unstructured":"Xavier Gabaix, Parameswaran Gopikrishnan, Vasiliki Plerou, and H\u00a0Eugene Stanley. 2003. A theory of power-law distributions in financial market fluctuations. Nature 423, 6937 (2003), 267\u2013270."},{"key":"e_1_3_2_1_18_1","first-page":"238","article-title":"Data transformation and self-exciting threshold autoregression","volume":"30","author":"Ghaddar DK","year":"1981","unstructured":"DK Ghaddar and H Tong. 1981. Data transformation and self-exciting threshold autoregression. Journal of the Royal Statistical Society Series C: Applied Statistics 30, 3 (1981), 238\u2013248.","journal-title":"Journal of the Royal Statistical Society Series C: Applied Statistics"},{"key":"e_1_3_2_1_19_1","volume-title":"The lambert way to gaussianize heavy-tailed data with the inverse of Tukey\u2019s h transformation as a special case. The Scientific World Journal 2015","author":"Goerg M","year":"2015","unstructured":"Georg\u00a0M Goerg. 2015. The lambert way to gaussianize heavy-tailed data with the inverse of Tukey\u2019s h transformation as a special case. The Scientific World Journal 2015 (2015)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s100510050292"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.60.5305"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1980.tb00297.x"},{"key":"e_1_3_2_1_23_1","volume-title":"Transformations: An introduction and a bibliography. International Statistical Review\/Revue Internationale de Statistique","author":"Hoyle H","year":"1973","unstructured":"Mike\u00a0H Hoyle. 1973. Transformations: An introduction and a bibliography. International Statistical Review\/Revue Internationale de Statistique (1973), 203\u2013223."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.3658902"},{"key":"e_1_3_2_1_25_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1086\/294632"},{"key":"e_1_3_2_1_27_1","volume-title":"Martin Meyer, and H\u00a0Eugene Stanley.","author":"Plerou Vasiliki","year":"1999","unstructured":"Vasiliki Plerou, Parameswaran Gopikrishnan, Luis A\u00a0Nunes Amaral, Martin Meyer, and H\u00a0Eugene Stanley. 1999. Scaling of the distribution of price fluctuations of individual companies. Physical review e 60, 6 (1999), 6519."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/61.1.177"},{"key":"e_1_3_2_1_29_1","volume-title":"Does the Box\u2013Cox transformation help in forecasting macroeconomic time series?International Journal of Forecasting 29, 1","author":"Proietti Tommaso","year":"2013","unstructured":"Tommaso Proietti and Helmut L\u00fctkepohl. 2013. Does the Box\u2013Cox transformation help in forecasting macroeconomic time series?International Journal of Forecasting 29, 1 (2013), 88\u201399."},{"key":"e_1_3_2_1_30_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Ramachandran Prajit","year":"2018","unstructured":"Prajit Ramachandran, Barret Zoph, and Quoc\u00a0V. Le. 2018. Searching for Activation Functions. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=Hkuq2EkPf"},{"key":"e_1_3_2_1_31_1","volume-title":"International conference on machine learning. PMLR, 1530\u20131538","author":"Rezende Danilo","year":"2015","unstructured":"Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In International conference on machine learning. PMLR, 1530\u20131538."},{"key":"e_1_3_2_1_32_1","volume-title":"Warped gaussian processes. Advances in neural information processing systems 16","author":"Snelson Edward","year":"2003","unstructured":"Edward Snelson, Zoubin Ghahramani, and Carl Rasmussen. 2003. Warped gaussian processes. Advances in neural information processing systems 16 (2003)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2018.1489139"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.4310\/CMS.2010.v8.n1.a11"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2017.04.001"},{"key":"e_1_3_2_1_36_1","first-page":"203","article-title":"Financial returns modelled by the product of two stochastic processes-A study of the daily sugar prices 1961-75","volume":"1","author":"Taylor SJ","year":"1982","unstructured":"SJ Taylor. 1982. Financial returns modelled by the product of two stochastic processes-A study of the daily sugar prices 1961-75. Time Series Analysis: Theory and Practice 1 (1982), 203\u2013226.","journal-title":"Time Series Analysis: Theory and Practice"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0927-5398(02)00052-X"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the NSF-Sponsored Regional Research Conference, Vol.\u00a07","author":"Tukey W","year":"1977","unstructured":"John\u00a0W Tukey. 1977. Modern techniques in data analysis. In Proceedings of the NSF-Sponsored Regional Research Conference, Vol.\u00a07. Southern Massachusetts University North Dartmouth, MA, USA."},{"key":"e_1_3_2_1_39_1","volume-title":"Bounds on normal approximations to Student\u2019s and the chi-square distributions. The Annals of Mathematical Statistics","author":"Wallace L","year":"1959","unstructured":"David\u00a0L Wallace. 1959. Bounds on normal approximations to Student\u2019s and the chi-square distributions. The Annals of Mathematical Statistics (1959), 1121\u20131130."},{"key":"e_1_3_2_1_40_1","volume-title":"Parsimonious quantile regression of financial asset tail dynamics via sequential learning. Advances in neural information processing systems 31","author":"Yan Xing","year":"2018","unstructured":"Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, and Qi Wu. 2018. Parsimonious quantile regression of financial asset tail dynamics via sequential learning. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/87.4.954"}],"event":{"name":"ICAIF '23: 4th ACM International Conference on AI in Finance","acronym":"ICAIF '23","location":"Brooklyn NY USA"},"container-title":["4th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604237.3626870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:38:37Z","timestamp":1755884317000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,25]]},"references-count":41,"alternative-id":["10.1145\/3604237.3626870","10.1145\/3604237"],"URL":"https:\/\/doi.org\/10.1145\/3604237.3626870","relation":{},"subject":[],"published":{"date-parts":[[2023,11,25]]},"assertion":[{"value":"2023-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}