{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:52:56Z","timestamp":1791647576341,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":83,"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 develop a structural, dual-dimensional view of tail risk and show how to extract the return-relevant component from existing proxies. In a model with heterogeneous beliefs, observed tail-risk measures are noisy mixtures of ex-ante disaster concerns and ex-post realizations with underreaction. Guided by this framework, we use partial least squares (PLS) to construct an aggregate tail-risk index,\u00a0TRPLS\u00a0 , from 18 U.S. option-, return-, and macro-based proxies.\u00a0 TRPLS\u00a0\u00a0strongly and positively predicts the U.S. equity premium in- and out-of-sample, outperforms leading macro and uncertainty predictors, and delivers sizable gains in a mean\u2013variance asset-allocation exercise. Decompositions show that TRPLS\u00a0 loads on both ex-ante and ex-post channels, has stronger predictive power in low-sentiment and high-uncertainty states, and forecasts primarily cash-flow news rather than discount-rate news.<\/jats:p>","DOI":"10.2139\/ssrn.7593226","type":"posted-content","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:29:28Z","timestamp":1791646168000},"source":"Crossref","is-referenced-by-count":0,"title":["A Tale of Two Types of Tail Risk and Market 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