{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:49:41Z","timestamp":1791647381689,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"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>We propose a quantile factor-augmented heterogeneous autoregressive (QF-HAR) model for forecasting realized volatility. The QF-HAR model is constructed by incorporating common factors extracted from quantile factor models at different quantile levels into the HAR model. The proposed model enables us to utilize common variation across international stock markets under different market conditions in forecasting realized volatility, while reducing the dimensionality of the predictors. An out-of-sample forecast comparison is performed for the realized volatilities of major stock market indices using realized volatilities and returns from 14 international stock indices. The forecast results show that QF-HAR models based on appropriately selected quantile levels outperform the benchmark HAR and HAR-X models for both 1-step and 5-step ahead forecasts, and that the forecasting contribution of the common factors differs across quantile levels. The estimated quantile factor loadings further show that the exposures of stock market returns and realized volatilities to the common factors used for forecasting vary across quantile levels.<\/jats:p>","DOI":"10.2139\/ssrn.7593056","type":"posted-content","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:10:37Z","timestamp":1791645037000},"source":"Crossref","is-referenced-by-count":0,"title":["Forecasting realized volatility with a quantile factor-augmented heterogeneous autoregressive model"],"prefix":"10.2139","author":[{"given":"Seunghwan","family":"Shin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieun","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:10:37Z","timestamp":1791645037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7593056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7593056","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}