{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:32:22Z","timestamp":1742913142561,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031624940"},{"type":"electronic","value":"9783031624957"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62495-7_38","type":"book-chapter","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:24Z","timestamp":1719001164000},"page":"504-517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Forecast Distributions in\u00a0Neural Network HAR-Type Models for\u00a0Range-Based Volatility"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8768-4606","authenticated-orcid":false,"given":"Michele","family":"La Rocca","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8496-0321","authenticated-orcid":false,"given":"Cira","family":"Perna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"issue":"4","key":"38_CR1","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.3982\/ECTA13085","volume":"85","author":"J Jacod","year":"2017","unstructured":"Jacod, J., Li, Y., Zheng, X.: Statistical properties of microstructure noise. Econometrica 85(4), 1133\u20131174 (2017)","journal-title":"Econometrica"},{"key":"38_CR2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1086\/296072","volume":"53","author":"M Garman","year":"1980","unstructured":"Garman, M., Klass, M.: On the estimation of security price volatilities from historical data. J. Bus. 53, 67\u201378 (1980)","journal-title":"J. Bus."},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Korkusuz, B., Kambouroudis, D., McMillan, D. G.: Do extreme range estimators improve realized volatility forecasts? Evidence from G7 stock markets. Finance Res. Lett. 55, 103992 (2023)","DOI":"10.1016\/j.frl.2023.103992"},{"issue":"9","key":"38_CR4","first-page":"873","volume":"25","author":"TG Bali","year":"2005","unstructured":"Bali, T.G., Weinbaum, D.: A comparative study of alternative extreme-value volatility estimators. J. Futures Mark. Futures, Options, Other Deriv. Prod. 25(9), 873\u2013892 (2005)","journal-title":"J. Futures Mark. Futures, Options, Other Deriv. Prod."},{"issue":"3","key":"38_CR5","first-page":"297","volume":"26","author":"J Shu","year":"2006","unstructured":"Shu, J., Zhang, J.E.: Testing range estimators of historical volatility. J. Futures Mark. Futures, Opt. Other Derivat. Prod. 26(3), 297\u2013313 (2006)","journal-title":"J. Futures Mark. Futures, Opt. Other Derivat. Prod."},{"key":"38_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1086\/296071","volume":"53","author":"M Parkinson","year":"1980","unstructured":"Parkinson, M.: The extreme value method for estimating the variance of the rate of return. J. Bus. 53, 61\u201365 (1980)","journal-title":"J. Bus."},{"issue":"2","key":"38_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.frl.2010.12.002","volume":"8","author":"H Li","year":"2011","unstructured":"Li, H., Hong, Y.: Financial volatility forecasting with range-based autoregressive volatility model. Financ. Res. Lett. 8(2), 69\u201376 (2011)","journal-title":"Financ. Res. Lett."},{"issue":"6","key":"38_CR8","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1002\/fut.20321","volume":"28","author":"J Jacob","year":"2008","unstructured":"Jacob, J.: Vipul: estimation and forecasting of stock volatility with range-based estimators. J. Fut. Mark. 28(6), 561\u2013581 (2008)","journal-title":"J. Fut. Mark."},{"issue":"11","key":"38_CR9","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1002\/fut.21643","volume":"34","author":"IM Jiang","year":"2014","unstructured":"Jiang, I.M., Hung, J.C., Wang, C.S.: Volatility forecasts: do volatility estimators and evaluation methods matter? J. Futur. Mark. 34(11), 1077\u20131094 (2014)","journal-title":"J. Futur. Mark."},{"key":"38_CR10","volume-title":"A Range-Based GARCH Model for Forecasting Volatility, MPRA Paper 21323","author":"DS Mapa","year":"2003","unstructured":"Mapa, D.S.: A Range-Based GARCH Model for Forecasting Volatility, MPRA Paper 21323. University Library of Munich, Germany (2003)"},{"issue":"4","key":"38_CR11","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1198\/073500106000000206","volume":"24","author":"MW Brandt","year":"2006","unstructured":"Brandt, M.W., Jones, C.S.: Volatility forecasting with range-based EGARCH models. J. Bus. Econ. Stat. 24(4), 470\u2013486 (2006)","journal-title":"J. Bus. Econ. Stat."},{"issue":"1","key":"38_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S1057-5219(02)00125-4","volume":"12","author":"L Chan","year":"2003","unstructured":"Chan, L., Lien, D.: Using high, low, open, and closing prices to estimate the effects of cash settlement on futures prices. Int. Rev. Financ. Anal. 12(1), 35\u201347 (2003)","journal-title":"Int. Rev. Financ. Anal."},{"issue":"3","key":"38_CR13","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1353\/mcb.2005.0027","volume":"37","author":"RY Chou","year":"2005","unstructured":"Chou, R.Y.: Forecasting financial volatilities with extreme values: the conditional autoregressive range (CARR) model. J. Money, Credit, Bank. 37(3), 561\u2013582 (2005)","journal-title":"J. Money, Credit, Bank."},{"issue":"2","key":"38_CR14","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1093\/jjfinec\/nbp001","volume":"7","author":"F Corsi","year":"2009","unstructured":"Corsi, F.: A simple approximate long-memory model of realized volatility. J. Financ. Economet. 7(2), 174\u2013196 (2009)","journal-title":"J. Financ. Economet."},{"issue":"8","key":"38_CR15","doi-asserted-by":"publisher","first-page":"3871","DOI":"10.1016\/j.csda.2006.03.003","volume":"51","author":"F Giordano","year":"2007","unstructured":"Giordano, F., La Rocca, M., Perna, C.: Forecasting nonlinear time series with neural network sieve bootstrap. Comput. Stat. Data Anal. 51(8), 3871\u20133884 (2007)","journal-title":"Comput. Stat. Data Anal."},{"issue":"3","key":"38_CR16","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1080\/10485252.2011.561344","volume":"23","author":"F Giordano","year":"2011","unstructured":"Giordano, F., La Rocca, M., Perna, C.: Properties of the neural network sieve bootstrap. J. Nonparametric Stat. 23(3), 803\u2013817 (2011)","journal-title":"J. Nonparametric Stat."},{"key":"38_CR17","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1111\/1540-6261.00454","volume":"57","author":"S Alizadeh","year":"2002","unstructured":"Alizadeh, S., Brandt, M.W., Diebold, F.X.: Range-based estimation of stochastic volatility models. J. Financ. 57, 1047\u20131091 (2002)","journal-title":"J. Financ."},{"issue":"2","key":"38_CR18","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1111\/1468-0262.00418","volume":"71","author":"TG Andersen","year":"2003","unstructured":"Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P.: Modeling and forecasting realized volatility. Econometrica 71(2), 579\u2013625 (2003)","journal-title":"Econometrica"},{"issue":"1","key":"38_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3483596","volume":"55","author":"W Ge","year":"2022","unstructured":"Ge, W., Lalbakhsh, P., Isai, L., Lenskiy, A., Suominen, H.: Neural network-based financial volatility forecasting: a systematic review. ACM Comput. Surv. (CSUR) 55(1), 1\u201330 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"3","key":"38_CR20","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/isaf.1455","volume":"26","author":"G Petneh\u00e1zi","year":"2019","unstructured":"Petneh\u00e1zi, G., G\u00e1ll, J.: Exploring the predictability of range-based volatility estimators using recurrent neural networks. Intell. Syst. Account. Finan. Manage. 26(3), 109\u2013116 (2019)","journal-title":"Intell. Syst. Account. Finan. Manage."},{"issue":"6","key":"38_CR21","first-page":"659","volume":"25","author":"J Kim","year":"2018","unstructured":"Kim, J., Baek, C.: Neural network heterogeneous autoregressive models for realized volatility. Commun. Stat. Appl. Methods 25(6), 659\u2013671 (2018)","journal-title":"Commun. Stat. Appl. Methods"},{"issue":"4","key":"38_CR22","doi-asserted-by":"publisher","first-page":"987","DOI":"10.2307\/1912773","volume":"50","author":"RF Engle","year":"1982","unstructured":"Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4), 987\u20131007 (1982)","journal-title":"Econometrica"},{"issue":"3","key":"38_CR23","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1111\/j.1467-9892.2011.00780.x","volume":"33","author":"Z Zhou","year":"2012","unstructured":"Zhou, Z.: Measuring nonlinear dependence in time-series, a distance correlation approach. J. Time Ser. Anal. 33(3), 438\u2013457 (2012)","journal-title":"J. Time Ser. Anal."},{"issue":"337","key":"38_CR24","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1080\/01621459.1972.10481224","volume":"67","author":"RL Winkler","year":"1972","unstructured":"Winkler, R.L.: A decision-theoretic approach to interval estimation. J. Am. Stat. Assoc. 67(337), 187\u2013191 (1972)","journal-title":"J. Am. Stat. Assoc."},{"key":"38_CR25","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice (3rd ed), Monash University, Australia, vol. 23, no. 2 (2018)","DOI":"10.32614\/CRAN.package.fpp3"},{"key":"38_CR26","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1146\/annurev-statistics-062713-085831","volume":"1","author":"T Gneiting","year":"2014","unstructured":"Gneiting, T., Katzfuss, M.: Probabilistic forecasting. Ann. Rev. Stat. Appl. 1, 125\u2013151 (2014)","journal-title":"Ann. Rev. Stat. Appl."}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62495-7_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:24:35Z","timestamp":1719001475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62495-7_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031624940","9783031624957"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62495-7_38","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}