气候政策不确定性与大宗商品市场间风险溢出OA

Against the backdrop of major strategic objectives-including"carbon peak by 2030"and"carbon neutrality by 2060"-as well as the broader imperative of high-quality development,the risk spillover from climate policy uncertainty(CPU)to commodity markets has been intensifying.In this study,we develop a novel analytical framework that integrates the quantile vector autoregression(QVAR)-based quantile linkage approach with the Baruník and Křehlík(BK)spillover index methodology.Using data from nine Chinese commodity indices,we examine the heterogeneous risk spillover effects of CPU across distinct frequency domains and quantiles.Our findings reveal three key insights:First,risk spillo-vers exhibit pronounced short-term,high-frequency dominance;spillover intensity is significantly stronger during extreme downside states than under normal conditions,yielding a U-shaped pattern across quantiles.Moreover,negative shocks generate systematically greater spillover intensity than posi-tive shocks.Second,pronounced spillover peaks coincide temporally with landmark policy or exogenous e-vents-such as the adoption of the Paris Agreement,the outbreak of the COVID-19 pandemic,and the Russia-Ukraine conflict.Third,CPU functions initially as a net receiver of risk spillovers in the short term,but gradually transitions into a net transmitter over longer horizons.