{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:53:08Z","timestamp":1791503588169,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<div>\n This paper investigates volatility-managed portfolios over what is, to our\n<\/div>\n<div>\n knowledge, the longest daily sample studied in this context: 100 years of\n<\/div>\n<div>\n returns from the Center for Research in Security Prices (CRSP) database\n<\/div>\n<div>\n (1926-2025; 26,190 trading days). Working within a Heston (1993) stochastic\n<\/div>\n<div>\n volatility framework combined with Merton (1971) portfolio choice, we show\n<\/div>\n<div>\n that the expected Sharpe ratio (SR) gain from volatility targeting satisfies\n<\/div>\n<div>\n \u2206SR \u221d SR0 \u00d7 cv(\u03c3)2, where SR0 denotes the asset\u2019s base Sharpe ratio and\n<\/div>\n<div>\n cv(\u03c3) its volatility\u2019s coefficient of variation. Building on this result, we develop\n<\/div>\n<div>\n a Kelly-type allocation rule: Variance-Drain-Optimal Volatility management\n<\/div>\n<div>\n (VDOV), and validate it through a strict walk-forward protocol. VDOV\n<\/div>\n<div>\n delivers an out-of-sample Sharpe ratio of 1.01, compared with 0.94 for the\n<\/div>\n<div>\n Moreira and Muir (2017) volatility target and 0.79 for buy-and-hold. We also\n<\/div>\n<div>\n uncover a structural break near 1985: before that date, volatility scaling was on\n<\/div>\n<div>\n average harmful (\u2206SR = \u22120.047), but afterward it became reliably beneficial\n<\/div>\n<div>\n (\u2206SR = +0.058; p = 0.014), a shift we attribute to the growth of derivatives\n<\/div>\n<div>\n markets and financial deregulation. Panel regressions on 1,067 asset-window\n<\/div>\n<div>\n observations across 21 assets confirm that both the variance drain channel\n<\/div>\n<div>\n (t = 7.09) and the leverage effect (t = 18.60) independently predict Sharpe\n<\/div>\n<div>\n ratio improvements. International tests reinforce these conclusions: VDOV\n<\/div>\n<div>\n outperforms both benchmarks out of sample in four of five Kenneth French\n<\/div>\n<div>\n regional markets (80%)\n<\/div><\/jats:p>","DOI":"10.2139\/ssrn.7585278","type":"posted-content","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:41:28Z","timestamp":1791502888000},"source":"Crossref","is-referenced-by-count":0,"title":["Volatility Targeting, Variance Drain, and the Financialization Effect: A Century of Evidence"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9343-2895","authenticated-orcid":true,"given":"Sparsh","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:41:28Z","timestamp":1791502888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7585278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7585278","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}