{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T07:42:27Z","timestamp":1792222947102,"version":"4.4.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":37,"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-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This paper revisits momentum crash risk in developed and emerging equity markets using monthly Fama-French international factor data through April 2026. We distinguish conditional momentumreturn regressions from direct crash-probability models, define the investment rules completely, and impose strictly ex-ante portfolio scaling. Lower-tail momentum events cluster after weak market states, but the evidence is more precise in developed markets and weakens under alternative definitions and after 2010. Volatility management materially improves full-sample downside outcomes: the developed-market Sharpe ratio rises from 0.533 to 0.865 and maximum drawdown improves from-43.61% to-10.74%; the emerging-market Sharpe rises from 0.980 to 1.289. These gains are not uniform over time, particularly in emerging markets after 2010. The results support state-aware momentum risk control while cautioning against interpreting conditional associations as stable forecasting laws.<\/jats:p>","DOI":"10.2139\/ssrn.7612298","type":"posted-content","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T05:19:21Z","timestamp":1792127961000},"source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Momentum Crashes: Volatility Timing across Developed and Emerging 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