{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:45:21Z","timestamp":1792514721180,"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:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This study examines how geopolitical risk, trade war uncertainty, and climate policy uncertainty, used here as a proxy for environmental shocks, relate to the volatility and cross market dependence of United States equities and crude oil, using monthly data from January 1990 to December 2024. We combine a Markov switching GARCH framework augmented with exogenous shock indices, a dynamic conditional correlation model, and a generalized forecast error variance decomposition, and we add a long short term memory network as a predictability check. The baseline specification identifies two weakly separated volatility states for the S&amp;amp;P 500 and a single state for oil. In the extended specification, the response of equity volatility to all three shocks is larger in the crisis state, with climate policy uncertainty carrying the largest coefficient, whereas the mean response of equity returns to trade war uncertainty changes from positive in the stable state to negative in the crisis state. The conditional correlation between equity and oil returns averages 0.058 and peaks at 0.231 around 2020, and equities are net transmitters of spillovers to oil, at 2.60 percentage points, with the rolling spillover index rising during the Global Financial Crisis and the pandemic. The neural network yields negative out of sample R squared values, which is consistent with weak return predictability. The findings support regime aware hedging and macroprudential monitoring, subject to the estimation caveats discussed in the paper.<\/jats:p>","DOI":"10.2139\/ssrn.7635900","type":"posted-content","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:28:26Z","timestamp":1792513706000},"source":"Crossref","is-referenced-by-count":0,"title":["Geopolitical Risk, Trade War Uncertainty, and Environmental Shocks: Regime Switching Volatility and Dynamic Spillovers in Global Financial and Commodity Markets"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5889-1474","authenticated-orcid":true,"given":"Owosuyi","family":"Bamidele","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chisom","family":"Chinedu-Onuoha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritabrata","family":"Bhattacharyya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:28:26Z","timestamp":1792513706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7635900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7635900","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}