{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T03:32:05Z","timestamp":1793417525985,"version":"4.5.0"},"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>This paper investigates whether combining conditional volatility, filtered market-regime probabilities, and extreme-loss severity through regime-conditioned entropy weights improves equity-market stress measurement. An Adaptive Composite Market Stress Index (A-CMSI) is constructed for AAPL, MSFT, and SPY from ARIMA-conditional-variance estimates, forward-filtered five-state Gaussian hidden Markov probabilities and rolling peaks-over-threshold generalized Pareto expected shortfall. An optional specification adds cross-asset correlation. A chronological expanding-window design uses 2,765 adjusted-price observations from 2015 through 2025, monthly model refits, and mature-label forecast calibration. Across 1,810 held-out 30-day volatility forecasts, Adaptive yields RMSE 0.10532, compared with 0.10457 for equal weighting and 0.10411 for the No HMM ablation. The Adaptive-Equal Diebold-Mariano test does not reject equal predictive accuracy (p = 0.217). Adaptive achieves a future 30-day crisis ROC AUC of 0.658 and an after-close same-day crisis-detection F1 of 0.300 at an expanding 90th-percentile signal threshold; these are distinct evaluation tasks. Rolling EVT and wavelet diagnostics characterize tails and timescale dependence, while external-index comparisons remain exploratory because revised observations and assumed publication lags are used. The findings support a reproducible stress-measurement framework but do not establish that adaptive weighting outperforms simpler composites.<\/jats:p>","DOI":"10.2139\/ssrn.7670199","type":"posted-content","created":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T03:23:46Z","timestamp":1793417026000},"source":"Crossref","is-referenced-by-count":0,"title":["Does Adaptive Weighting Improve Equity Market Stress Measurement? Evidence from a GARCH-HMM-EVT Composite Index"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0665-6716","authenticated-orcid":true,"given":"Ayush","family":"Anand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T03:23:46Z","timestamp":1793417026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7670199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7670199","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}