{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:53:41Z","timestamp":1791586421298,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":13,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This paper studies whether the calendar-time occupation of the intraday price path contains useful information for realized variance forecasting. We construct occupation-spread predictors from symmetric quantile differences of the within-block occupation distribution, so that a price level receives weight according to the time for which the path remains there. At \u03b1 = 0, the daily measure is the sum of squared local high\u2013low ranges and satisfies DQ_t(0) = 4 log(2) PK_t; for \u03b1 &amp;gt; 0, local extrema are replaced by interior occupation quantiles. Under a local Brownian approximation, the expected statistic is proportional to integrated variance conditional on local volatility, giving a variance-scale interpretation. We evaluate the predictors in HAR-X models for 30 large-cap U.S. stocks using monthly rolling forecasts from February 2015 through December 2025. Adding a validation-selected occupation spread to a HAR model already containing Parkinson and Garman\u2013Klass predictors lowers both QLIKE and HMSE for all 30 stocks, with mean loss ratios of 0.9728 and 0.8969. The results indicate that interior occupation structure adds predictive content beyond standard return- and range-based volatility predictors.<\/jats:p>","DOI":"10.2139\/ssrn.7589199","type":"posted-content","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T13:39:02Z","timestamp":1791553142000},"source":"Crossref","is-referenced-by-count":0,"title":["Time-Occupation Information for Realized Volatility Forecasting"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5320-4617","authenticated-orcid":true,"given":"Brian Sing Fan","family":"Chan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5295-1762","authenticated-orcid":true,"given":"Kam-Fung","family":"Cheung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5893-0695","authenticated-orcid":true,"given":"Nia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"885","DOI":"10.2307\/2527343","article-title":"Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts","volume":"39","author":"Torben G Andersen","year":"1998","journal-title":"International Economic Review"},{"issue":"2","key":"ref2","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/1468-0262.00418","article-title":"Modeling and Forecasting Realized Volatility","volume":"71","author":"Torben G Andersen","year":"2003","journal-title":"Econometrica"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jeconom.2015.10.007","article-title":"Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting","volume":"192","author":"Tim Bollerslev","year":"2016","journal-title":"Journal of Econometrics"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.jeconom.2006.06.012","article-title":"Realized Range-Based Estimation of Integrated Variance","volume":"141","author":"Kim Christensen","year":"2007","journal-title":"Journal of Econometrics"},{"issue":"5","key":"ref5","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1093\/jjfinec\/nbac020","article-title":"A Machine Learning Approach to Volatility Forecasting","volume":"21","author":"Kim Christensen","year":"2023","journal-title":"Journal of Financial Econometrics"},{"issue":"2","key":"ref6","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1093\/jjfinec\/nbp001","article-title":"A Simple Approximate Long-Memory Model of Realized Volatility","volume":"7","author":"Fulvio Corsi","year":"2009","journal-title":"Journal of Financial Econometrics"},{"issue":"3","key":"ref7","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.ijforecast.2019.09.001","article-title":"Bias Corrections for Exponentially Transformed Forecasts: Are They Worth the Effort?","volume":"36","author":"Matei Demetrescu","year":"2020","journal-title":"International Journal of Forecasting"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1086\/296072","article-title":"On the Estimation of Security Price Volatilities from Historical Data","volume":"53","author":"Mark B Garman","year":"1980","journal-title":"Journal of Business"},{"key":"ref9","article-title":"Do Extreme Range Estimators Improve Realized Volatility Forecasts? Evidence from G7 Stock Markets","volume":"55","author":"Burak Korkusuz","year":"2023","journal-title":"Finance Research Letters"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.jeconom.2006.05.019","article-title":"Measuring Volatility with the Realized Range","volume":"138","author":"Martin Martens","year":"2007","journal-title":"Journal of Econometrics"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1086\/296071","article-title":"The Extreme Value Method for Estimating the Variance of the Rate of Return","volume":"53","author":"Michael Parkinson","year":"1980","journal-title":"Journal of Business"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jeconom.2010.03.034","article-title":"Volatility Forecast Comparison Using Imperfect Volatility Proxies","volume":"160","author":"Andrew J Patton","year":"2011","journal-title":"Journal of Econometrics"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.najef.2018.06.010","article-title":"Quantile Range-Based Volatility Measure for Modelling and Forecasting Volatility Using High Frequency Data","volume":"47","author":"Shay-Kee Tan","year":"2019","journal-title":"North American Journal of Economics and Finance"}],"container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:34:58Z","timestamp":1791585298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7589199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":13,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7589199","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}