This paper employs a proprietary data set on commodity producers’ profit margins (PPMG) and establishes a robust positive relationship between commodity producers’ profitability growth and future returns of commodity futures. The spread portfolio that longs top-PPMG futures contracts and shorts bottom-PPMG futures contracts delivers a statistically significant average weekly return of 36 basis points. We further demonstrate that profitability is a strong SDF factor in commodity futures market. We theoretically justify our empirical findings by developing an investment-based pricing model, in which producers optimally adjust their production process by maximizing profits subject to aggregate profitability shocks. The model reproduces key empirical results through calibration and simulation.

Based on high-frequency data of China's commodity market from 2017 to 2022, this article examines the intraday momentum effect. The results indicate that China's commodity futures and options have significant intraday reversal effects, and the overnight opening factor and opening to last half hour factor are more significant. These effects are driven, in part, by liquidity factors. This trend aligns with market makers' behavior, passively accepting orders during low liquidity and actively closing positions amid high liquidity. Furthermore, our examination of cross-predictive ability shows strong futures-to-options predictability, while the reverse is weaker. We posit options traders' Vega hedging as a key factor in this phenomenon, our study finds futures volatility changes can predict options’ return.

Against the backdrop of the United Kingdom's withdrawal from the European Union (BREXIT), this study examines predictability in the stock markets of sixteen European countries, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa) by analyzing how their returns predict the returns of sixteen commodities at different quantile levels. The study builds upon existing literature on predictability and extends it by investigating the impact of the BREXIT crisis on these markets. The findings suggest that investors can hedge their portfolios with various commodities during times of the BREXIT crisis, but caution is advised, and the trend of both equities and commodities should be closely monitored before making investment decisions.

The objective of this study is to create optimal two-asset portfolios consisting of stocks from Western Europe, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa), as well as sixteen commodity types during the BREXIT period. We utilized dynamic variances and covariances from the GARCH model to derive weights for the two-asset portfolios, with each portfolio consisting of one equity factor and one commodity factor. Subsequently, hedge ratios were calculated for these various assets. Our findings indicate that portfolios consisting of European stocks do not require the inclusion of commodities, whereas the other equities do.

China commodity futures market network is constructed. Commodity is the node of the network, and the network link is defined by the price correlation matrix. We analyze the relationship between the centrality of each commodity in the commodity futures market network and the optimal weight of the commodity portfolio, empirically examine the market system factors and commodity personalized factors that affect the centrality of commodity, and evaluate the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. It is found that the commodities with high network centrality are often related to industrial products and have high volatility. Commodities with higher centrality have lower portfolio weights. We put forward a kind of commodity futures investment strategy based on network, according to the network centricity grouping the commodities, the network centricity lower edge of the commodity structure of the portfolio, cumulative yield is better than that of centricity higher core product portfolio, the whole market portfolio yield, but due to large maximum retracement, lead to the stability and ability to resist risk compared with the other two groups of goods combination. The main contribution of this paper is to optimize portfolio selection by establishing the relationship between portfolio weight and commodity centrality by using commodity futures market network as a tool.

Referring to the newspaper textual analysis method by Baker et al. (2016), this study constructs a monthly Chinese Real Estate Policy Uncertainty (REPU) index from 2001 to 2018. The index increases significantly near the promulgation of major policies. We also conduct evaluation of the index with the vector autoregression (VAR) model, which reveals that the rise of REPU indicates the decline in the growth rate of commodity housing development investment, sales area, and real estate industry added value. The REPU index is helpful to expand the understanding of policy uncertainty, and the accurate measurement of REPU is the basis for further research of its impact on China's real estate market.

In Chinese culture, digit 8 (4) is taken as lucky (unlucky). We find that the numerological superstition has a profound impact across China’s stock, bond, foreign exchange and commodities markets, affecting asset prices in both the primary and secondary markets. The superstition effect, i.e., the probability of asset price ending with a lucky (unlucky) digit far exceeds (falls short of) what would be expected by chance, is prevalent. The effect is driven by investors’ reliance on superstition as an anchor to face uncertainty in asset pricing and the overoptimism of unsophisticated investors. While the superstition effect does not lead to systemic mispricing for assets traded by sophisticated investors, it implies overpricing for assets involving more unsophisticated investors.

Although China has the largest trading volume of commodity futures, limited studies have been devoted to the term structure of Chinese commodity futures. This paper takes the tools in functional data analysis to understand the term structure of commodity futures and forecast its dynamic changes at both short and long horizons. Functional ANOVA has been applied to examine the calendar e_ect of term structure in level and _nd the seasonality in the commodity futures of coking coal and polypropylene. We use an h-step functional autoregressive (1) model to forecast the dynamic change of term structure. Comparing with native predictor, in-sample and out-of-sample forecasting performance indicate that additional forecasting power is gained by using the functional autoregressive structure. Although the dynamic change at short horizons is not predictable, the forecasts appear much accurate at long horizons due to the stronger temporal dependence. The predictive factor method has a better in-sample _tting, but it cannot outperform the estimated kernel method for out-of-sample testing, except for 1-quarter-ahead forecasting.

through the analysis of the input and output of goods, industrial chain is considered in this paper: for any commodity in the industry chain and GDP, and the final product price are equal, so the impact on the consumption of GDP is directly; influence of investment to GDP is more complicated, because the investment specific effects on the GDP not only depends on the input factor, output rate, organic composition of capital, need to realize through the consumption way to; the use of modern computing technology, analysis of different commodities industry chain, can accurately measure related to consumption, investment on GDP specific effects, related to the entity economy monetary demand.

In recent times, the prices of internationally-traded commodities have reached record highs and are expected to continue growing in the foreseeable future. This phenomenon is partially driven by strong demand from a small number of emerging economies, such as China and India. This paper places the recent commodity price boom in historical context, drawing on an investigation of the long-term time-series properties, and presents unique features for 33 individual commodity prices. Using a new methodology for examining cross-sectional variation of commodity returns and its components, we find strong evidence that the prices of world primary commodities are extremely volatile. In addition, prices are roughly 30 percent more volatile under floating than under fixed exchange rate regimes. Finally, using the capital asset pricing model as a loose framework, we find that global macroeconomic risk components have become relatively more important in explaining commodity price volatility.