{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T04:50:39Z","timestamp":1791348639790,"version":"4.3.1"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":25,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We revisit the asymmetric relation between downside price moves and future volatility, and the many attempts to turn the same asymmetry into return forecasts, in Korea and the United States under a common design with internally frozen trading rules, a held-out 2021-2026 test period, and multiple-testing corrections. Downside realized semivariance predicts future upside, downside, and total variance, while upside semivariance does not; the effect survives Holm adjustment in both markets over 2005-2020 and replicates out of sample in the U.S. but not in Korea. Contrary to the common presumption, the leverage effect does not strengthen in turbulent regimes; in the U.S. it weakens. By contrast, directional asymmetry measures built from daily returns-monthly relative signed jump variation and weekly realized skewness-carry no return information beyond short-term reversal: their rank correlation with the same-period return is 0.76-0.87, and their Fama-MacBeth coefficients vanish once reversal is controlled for. The dominant direction of tomorrow's variation cannot be forecast better than a naive baseline, and stock-level forecasts of upside and downside semivariance rank stocks almost identically. A crosscountry difference-indifferences analysis of the 2023-2025 Korean short-sale ban finds no change in the cross-sectional pricing of these measures, although the test has limited power. Overall, asymmetry is useful for forecasting risk, not returns.<\/jats:p>","DOI":"10.2139\/ssrn.7548198","type":"posted-content","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T17:28:36Z","timestamp":1790875716000},"source":"Crossref","is-referenced-by-count":0,"title":["Downside Volatility Predicts Risk, Not Returns: Out-of-Sample Evidence from Korea and the U.S"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5366-3408","authenticated-orcid":true,"given":"Dong Gyu","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jfineco.2015.02.009","article-title":"Does realized skewness predict the cross-section of equity returns","volume":"118","author":"Diego Amaya","year":"2015","journal-title":"Journal of Financial Economics"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/j.1540-6261.2006.00836.x","article-title":"The cross-section of volatility and expected returns","volume":"61","author":"Andrew Ang","year":"2006","journal-title":"Journal of Finance"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.jfineco.2010.08.014","article-title":"Maxing out: Stocks as lotteries and the cross-section of expected returns","volume":"99","author":"G Turan","year":"2011","journal-title":"Journal of Financial Economics"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1093\/acprof:oso\/9780199549498.003.0007","article-title":"Measuring downside risk: Realised semivariance","author":"Ole E Barndorff-Nielsen","year":"2010","journal-title":"Volatility and Time Series Econometrics: Essays in Honor of Robert F. 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