This paper revisits two widely studied predictors of commodity futures returns, basis and basis momentum, whose conventional measures using first-nearby futures as proxies for spot prices may limit their ability to capture fundamental spot-market risks. Motivated by this limitation, we construct two spot-based signals from observed spot and futures prices, which are theoretically shown to contain incremental information beyond conventional measures. Using 41 Chinese commodity futures, we find that these signals robustly predict first-nearby contract returns and remain significantly priced in time-series and cross-sectional tests, even after controlling for their conventional counterparts. We then develop a spot-enhanced three-factor model, including the market factor and the two spot-based factors, which consistently outperforms three widely used benchmark models in pricing competing factors and explaining return anomalies.

This study examines the price discovery patterns in the three BRICS countries’ stock index futures markets that were launched after 2000 – China, India, and Russia. We detect two structural breaks in these three futures price series and their underlying spot price series, and use them to form subsamples. Employing a Vector Error Correction Model (VECM) and the Hasbrouck (1995) test, we find the price discovery function of stock index futures markets generally improves over time in China and India, but declines in Russia. A closer examination not only confirms the findings of Yang et al. (2012) and Hou and Li (2013) regarding price discovery in China’s stock index markets, but also reveals the inconsistency of futures’ leading role in the price discovery process. Further, we find some evidence of day-of-the-week effects in earlier part of the sample in China, but not in India or Russia. And our GARCH model results show bidirectional volatility spillover between futures and spot in China and India, but only unidirectional in Russia.

This study estimates optimal hedge ratios using various econometric models. These models are evaluated based on the in- and out-of-sample optimal hedge ratio forecasts. Using daily data of spot and futures 1-month, 3-month, 6-month prices of aluminum and copper in the Shanghai Futures Exchange, the optimal hedge ratios are calculated from the OLS regression model, the VAR with error correction model, the bivariate GARCH model and the Exponential GARCH (EGARCH) Model. Hedging performance in terms of variance reduction of returns from four different models are also conducted. It is found that the EGARCH hedge ratio provides the largest reduction in the variance of the return portfolio, but they do not perform better than the alternatives over the out-of-sample period.

The theory of storage states that the marginal convenience yield on inventory falls at a decreasing rate as inventory increases. Previous literature has tested this hypothesis using so-called “direct test” approach which employs a direct measurement of inventory levels, or the “ indirect test” approach which examines the relative variation of spot and futures prices and the relative variation of negative basis to positive basis as alternative proxies for inventory levels. The rationale behind “indirect test” is based on the hypothesis that futures prices are less variable than spot prices when inventory is low, and have similar variability when inventory is high. The authors propose a “ unified “ test of the theory of storage that incorporates aspects of both direct and indirect tests in an ARMAX-asymmetric GARCH model framework.