{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:51:54Z","timestamp":1791449514161,"version":"4.3.3"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":33,"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 study whether over-parameterized models are suitable for predicting realized volatility across a cross-section of 41 assets related to the Dow Jones Industrial Average index. We propose an unconstrained vector autoregressive (VAR) model and reach and surpass the interpolation point by increasing the number of lags. We estimate the model using ridgeless regression equation by equation on rolling windows of 650 and 1000 daily observations. We show that the conditions for benign overfitting hold and that, consistently across stocks, the predicted MSE exhibits the typical double descent pattern. We assess performance using QLIKE, MSE, and realized utility metrics, finding that the benchmark heterogenous autoregressive (HAR) model outperforms the ridgeless VAR model on average when both rely solely on realized volatilities and VIX as predictors. This result changes in particular sub-samples. We show that the proposed approach is the most accurate model in low-volatility regimes and, after the onset of the coronavirus pandemic, in high-volatility regimes as well, becoming economically profitable by up to dozens of basis points. In these regimes, the ridgeless model is justied by the large statistical and economic gains that offset the additional computational costs of its estimation.<\/jats:p>","DOI":"10.2139\/ssrn.7544001","type":"posted-content","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T15:03:23Z","timestamp":1790780603000},"source":"Crossref","is-referenced-by-count":0,"title":["Benign Overfitting in Realized Volatility Forecasting: A VAR approach"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2038-9362","authenticated-orcid":true,"given":"Francesco","family":"Audrino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1198\/016214501750332965","article-title":"The distribution of exchange rate volatility","volume":"96","author":"T G Andersen","year":"2001","journal-title":"Journal of the American Statistical Association"},{"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":"T G Andersen","year":"2003","journal-title":"Econometrica"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1080\/07474938.2015.1092801","article-title":"Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics","volume":"35","author":"F Audrino","year":"2016","journal-title":"Econometric Reviews"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ijforecast.2019.05.010","article-title":"The impact of sentiment and attention measures on stock market volatility","volume":"36","author":"F Audrino","year":"2020","journal-title":"International Journal of Forecasting"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1368-423X.2008.00275.x","article-title":"Realized kernels in practice: trades and quotes","volume":"12","author":"O E Barndor-Nielsen","year":"2009","journal-title":"The Econometrics Journal"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1111\/1467-9868.00336","article-title":"Econometric analysis of realized volatility and its use in estimating stochastic volatility models","volume":"64","author":"O E Barndor-Nielsen","year":"2002","journal-title":"Journal of the Royal Statistical Society. 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