{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:37:56Z","timestamp":1793111876638,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":60,"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 examines the risk spillover between new energy futures and AI industry chain assets and assess the hedging value of new energy futures. We divide AI assets into upstream computing infrastructure, midstream artificial intelligence, and downstream smart manufacturing. Based on the TVP-VAR time-frequency connectedness framework, we identify significant risk spillovers across different time frequencies between new energy futures and AI industry chain assets. We find that the spillovers over short horizons being considerably stronger than those observed over medium- and long-term horizons. From the perspective of each industry chain segment, midstream artificial intelligence and downstream smart manufacturing display strong risk-output characteristics, while upstream computing infrastructure is relatively weaker in risk-output capacity. We also find that industrial silicon futures provide strong hedging effectiveness for upstream computing infrastructure assets, whereas polysilicon and lithium carbonate futures demonstrate more significant risk diversification effects when allocated with midstream artificial intelligence assets and downstream smart manufacturing assets. Therefore, incorporating new energy futures into AI industry chain portfolios can effectively reduce portfolio risk, improve hedging efficiency, and enhance risk-adjusted returns. These findings offer useful implications for investment decisions and policy formulation.<\/jats:p>","DOI":"10.2139\/ssrn.7674954","type":"posted-content","created":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:08:52Z","timestamp":1793110132000},"source":"Crossref","is-referenced-by-count":0,"title":["Can New Energy Futures Serve as Effective Hedging Instruments for AI Industry Chain Assets? Evidence from China"],"prefix":"10.2139","author":[{"given":"liangfu","family":"liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianfang","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00036846.2026.2687744","article-title":"How artificial intelligence tokens interact with the energy sector: a novel perspective","author":"N Alsagr","year":"2026","journal-title":"Applied Economics"},{"issue":"4","key":"ref2","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1287\/mnsc.2021.3984","article-title":"Quantile connectedness: modeling tail behavior in the topology of financial networks","volume":"68","author":"T Ando","year":"2022","journal-title":"Management Science"},{"issue":"4","key":"ref3","doi-asserted-by":"crossref","DOI":"10.3390\/jrfm13040084","article-title":"Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions","volume":"13","author":"N Antonakakis","year":"2020","journal-title":"Journal of Risk and Financial Management"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.ribaf.2026.103340","article-title":"Economic policy uncertainty and AI-driven stock spillovers: Implications for portfolio diversification","volume":"85","author":"A Aslam","year":"2026","journal-title":"Research in International Business and Finance"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2025.128301","article-title":"Artificial intelligence, clean energy, and market integration: evidence from multiperiod quantile dynamics","volume":"397","author":"N Balci","year":"2026","journal-title":"Journal of Environmental Management"},{"issue":"2","key":"ref6","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1093\/jjfinec\/nby001","article-title":"Measuring the frequency dynamics of financial connectedness and systemic risk","volume":"16","author":"J Barun\u00edk","year":"2018","journal-title":"Journal of Financial Econometrics"},{"key":"ref7","author":"F Belhouichet","year":"2025","journal-title":"Tail Connectedness Between Robotics and AI ETFs and Traditional Us Assets Under Different Market Conditions: A Quantile Var Approach"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbankfin.2015.06.021","article-title":"Do commodities add value in multi-asset portfolios? 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