Journal of Commodity Markets最新文献

Using monthly data from January 1998 to May 2021, this study investigates the asymmetric impact of global economic policy uncertainty (GEPU) on international grain prices by using nonlinear autoregressive distribution lag (NARDL). We find that there is a positive, asymmetric relationship between GEPU and international grain prices. Specifically, GEPU has a greater negative than positive impact on wheat and maize prices, and the positive impact on soybean price is more pronounced than the negative one. We have also observed that the asymmetric impact of GEPU on rice price is not significant in the long run. The findings have important implications to formulate targeted and differentiated international grain price regulatory policies.

If the information held by the central bank is different from that of market participants, then the central bank’s announcement not only affects the view of monetary policy but also the view of economic fundamentals. This study investigates the information effects of monetary policy announcements on oil prices using a structural vector autoregression (VAR) model identified by sign restrictions. The sign restrictions rely on the high-frequency linkage between stock prices and interest rates surrounding the policy announcements. We find that a positive central bank information shock, which raises the interest rate by six basis points, leads to a 1.7% increase in oil prices within two months. We also find that central bank information shocks affect oil prices through the finance and expectation channels.

We construct daily realized volatility, skewness, and kurtosis using 5-min data of eight Chinese commodity futures and the Chinese stock market index from March 26, 2018 to October 22, 2020, then analyse the dynamic spillovers of realized moments among these markets. The results show that the spillover effects between commodity and stock markets intensify during shock periods such as ‘trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger than spillovers in skewness or spillovers in kurtosis; however, spillovers in higher-order moments seem to contain additional information. Shocks from the silver market influence realized moments of other markets. Soybean, corn, aluminium, and oil markets are affected by other markets. The contribution of wheat as a net transmitter to the system of spillovers between stock and commodity markets is only observed at higher-order realized moments. The results from OLS and quantile regressions show that the total spillovers are generally affected by the US stock market, economic uncertainties, and the COVID-19 outbreak.

This study examines the role of daily volatility persistence in transmitting information from macro-economy in the volatility of energy markets. In crude oil and natural gas markets, macro-economic factors, such as the VIX, the credit spread and the Baltic exchange dirty index, impact volatility, and this impact is channeled via the volatility persistence. Further, the impact of returns and variances is primarily transmitted to volatility via the daily volatility persistence. The dependence of volatility persistence on market and macro-economic conditions is termed conditional volatility persistence (CVP). The variation in daily CVP is economically significant, contributing up to 18% of future volatility and accounting for 29% of the model's explanatory power. Inclusion of the CVP in the model significantly improves volatility forecasts. Based on the utility benefits of volatility forecasts, the CVP adjusted volatility models provide up to 160 bps benefit to investors compared to the HAR models, even after accounting for transaction costs and varying trading speeds.

This paper proposes a novel quantile vector autoregressive extended joint connectedness framework to examine realized volatilities spillovers between oil and precious metals commodities using daily data from May 1st, 2006 until June 18th, 2021. Our findings suggest that crude oil is the main net transmitter of shocks in the network across all quartiles. The dynamic total connectedness is heterogeneous over time and driven by economic events. Interestingly, we see that the higher the quartile the more pronounced the net transmission mechanisms of realized volatilities. Notably, the net total directional and pairwise connectedness measures illustrate in most cases similar dynamics.

This paper examines the Silver Crisis of the late 1970s, which resulted in a $150 million lawsuit against the Hunt Brothers. In August 1988, the Hunt Brothers were found guilty by a jury of conspiracy, manipulation, monopolization, racketeering and fraud. Using a behavioural model, we aim to quantify the extent of manipulation in the silver market during the 1970s and the 1980s, with a specific focus on the period leading up to the Silver Crisis. Our behavioural model takes account of the role of fundamentals, manipulation and speculation. Our results indicate very little evidence of manipulation in the silver market in the run up to the Silver Crisis. Both fundamentals and speculation dominate the silver market during our sample, with speculation particularly important in the latter half of the 1970s. The distinction between manipulation and speculation is critical. While manipulation forces prices away from their fundamental value, speculation does not. Speculators certainly aim to take advantage of price changes but the actions are fully rational and consistent with the fundamental value of silver.

Fertilizer is an essential commodity traded in international and domestic markets and spatial competition is important feature impacting interfirm rivalry. In the case of North American fertilizer, numerous plants have been announced to either expand or open new plants (nitrogen-based fertilizer plants), exerting competitive pressures on an industry with surplus capacity but highly competitive in terms of production costs and technology. Proposed new plants and expansions are being induced by changes in the composition of crops, changes in the price of natural gas which affects the cost of producing domestic anhydrous ammonia. Developments in the fertilizer industry have become more volatile in the post-COVID period, and concurrent with the escalation in fuel prices, the Ukraine invasion, related embargoes on Russian trade, the world's largest exporter, and operations of the Grain Corridor. The purpose of this study is to quantify risks for plant expansion (brownfield and greenfield) of nitrogen fertilizer plants in North America, given the spatial competition and the corresponding dynamic market boundaries. Specifically, we quantify risks associated with fertilizer plant expansions, identify the optimal locations of new plants, and characterize spatial competition as a result of new entrants. A model is specified that integrates Geographical Information Systems (GIS) data into a stochastic mixed-integer network spatial optimization model using Monte Carlo simulations to account for risk in the random variables. The results are reprocessed into GIS for interpretation. The impact of risk in these variables results in market boundaries that are random. Specifically, competition for these new plants has embedded risks for new entrants on the probability of production and market penetration.

Jet fuel prices are highly volatile and outside airlines’ control, incentivizing them to reduce earnings volatility through financial hedging. Due to limited direct hedging options, airlines often resort to cross-hedging of jet fuel requirements. In this paper, we propose a composite jet fuel cross-hedging approach, shown to provide substantial performance benefits relative to a traditional single instrument strategy. A mimicking portfolio approach, incorporating multiple hedging instruments, is found to provide additional hedging gains. Finally, we demonstrate that further hedging effectiveness is achieved by adding recently introduced jet fuel swap contracts to a hedge portfolio.

In this paper, we theoretically study the issues that how nonlinear structures of energy market affect $C{O}_2$ costs of energy firms being passed through to energy prices in emission trading schemes. We set up oligopoly models to investigate $C{O}_2$ cost pass-through under different emission right allocations, characterize analytic derivations of $C{O}_2$ cost pass-through rates and then explore the connections between $C{O}_2$ pass-through rates and nonlinear market structures. Our findings suggest that the $C{O}_2$ cost pass-though rates are not only dependent on the elasticities of energy demand and supply, but largely determined by the convexity of demand curve and the competition intensity of energy market. More importantly, an economic curiosity ($C{O}_2$ cost pass-through overshifting) is also observed in the emission trading schemes under suitable market conditions. The existence of $C{O}_2$ cost pass-through overshifting provides regulators and policy makers important information that the emission trading schemes are at the risk of imperfect competition and may require further policy adjustments.

An alternative futures-based procedure is developed to forecast the season-average farm price for U.S. corn, an under-researched price forecast. The new method performs similarly or better than two widely-watched season-average price forecasts, i.e., the World Agricultural Supply and Demand Estimates and the Hoffman futures-based forecasts, at the beginning of the post-harvest season and just as well as those forecasts in most of the other months during the marketing year. We attribute the robust performance of the proposed forecast to its ability to use heterogeneous coefficients for futures and cash prices depending on the underlying market conditions. Improved performance of the proposed forecasts is especially noticeable when the market is more volatile. Overall, the method derived in this study complements the existing forecasts and provides valuable information for decision-makers.