The dynamic minimum variance hedge ratios (MVHRs) have been commonly estimated using the Bivariate GARCH model that overlooks the basis effect on the time-varying variance–covariance of spot and futures returns. This paper proposes an alternative specification of the BGARCH model in which the effect is incorporated for estimating MVHRs. Empirical investigation in commodity markets suggests that the basis effect is asymmetric, i.e., the positive basis has greater impact than the negative basis on the variance and covariance structure. Both in-sample and out-of-sample comparisons of the MVHR performance reveal that the model with the asymmetric effect provides greater risk reduction than the conventional models, illustrating importance of the asymmetric effect when modeling the joint dynamics of spot and futures returns and hence estimating hedging strategies.

We propose a new measure of price discovery, New Leadership Share (NLS), that attributes permanent information flow to individual markets using a uniquely identified structural moving average model. NLS quantifies each market’s contribution to permanent price innovations as a proportion of total informational leadership and offers key technical advantages, including uniqueness and adherence to standard statistical asymptotics. We derive closed-form solutions and analytical standard errors for bivariate markets and provide a framework that extends naturally to multiple markets without the variable ordering problem. Simulation results show that NLS consistently outperforms three widely used benchmarks. Empirical analysis of 2023 data finds that exchange-traded funds and front-month futures markets share equal leadership relative to the S&P 500 spot index.