Journal of Commodity Markets最新文献

In the last twenty years a large number of ethanol firms have established operations in the US. Ethanol, produced from corn, is blended with pure gasoline to produce fuel. Producers hold an option to turn off unprofitable plants. Blenders choose to substitute ethanol for gasoline at or beyond the mandate set by the government. We propose an equilibrium model for blenders and producers that accounts for government measures and for real optionality embedded in the industry. The model, driven by corn and gasoline prices, leads to analytical expressions for the price and output of ethanol, and to policy implications on the impacts of the mandate and blend credit. The model also leads to closed form valuation for an ethanol producer. Using data between 2000 and 2017 we confirm that, as in the model, ethanol was largely priced as the maximum of rescaled gasoline and corn prices. Historical output levels between the mandate and installed capacity were explained by our theory. Finally, the share price dynamics for the largest public ethanol producer in the US was consistent in some aspects with the value of a real option.

We analyze Chicago based daily wheat price volatility over more than 140 years using a novel data set of daily high and low futures prices starting in 1877. We identify five long-run regimes and find that volatility shifts between regimes are statistically more pronounced than fluctuations within regimes, even when conditioning on economic states. Historical volatility estimates derived from average commodity price data, a common practice in empirical studies, exhibit a regime-dependent upward bias between 0% and 22%. The magnitude of the bias and the importance of regimes potentially explain contradictory findings on volatility patterns in earlier studies.

This paper assesses the accuracy of several machine learning models’ predictions of the gold risk premium when using an extensive set of 186 predictors. We perform an out-of-sample evaluation and consider both statistical and portfolio metrics. Our results show that machine learning methods and forecast combinations have a limited ability to outperform the historical mean when predicting the gold risk premium. Slightly better results are obtained when predictors are used individually. More specifically, we find that several technical indicators (moving average and momentum series) have forecasting power during periods of expansion, while several business cycle variables and geopolitical risk variables help predict the gold risk premium during recessions. An economic evaluation accounting for transaction costs shows that investors using machine learning methods to estimate expected returns on gold should anticipate limited portfolio gains.

The pronounced inflow of financial capital from index investors over the last 15 years and the accompanying substantial fluctuations in commodity futures markets have aroused public and academic interest. A common accusation made in this context is that commodity index traders (CITs) negatively influence the quality of commodity futures markets and keep them far from fundamentally justified price levels. In this paper, we focus on quantifying market efficiency, and investigate empirically the suggested effect of CITs over the period from 1999 to 2019 for 34 commodity futures markets. In contrast to recent studies, we find empirical evidence that the financialization positively affected the market efficiency of indexed commodity futures markets. Consistently, we observe that the degree of commodity index trader activity is associated with higher degrees of informational efficiency.

This study assesses the tracking performance of several futures-backed commodity exchange-traded funds (ETFs), single commodity exchange-traded notes (ETNs), and commodity sector ETNs relevant to agricultural market participants. We decompose total tracking errors into managerial and arbitrage components. Our findings reveal that the arbitrage process is the primary driver of observed tracking errors. ETNs tend to exhibit much larger tracking errors than ETFs. The tracking performance was not substantially different across agricultural and energy ETFs nor across single commodity and commodity sector ETNs. Using a GARCH model, the study reveals greater persistence of tracking errors for ETNs than ETFs. Roll dates do not significantly affect the volatility of tracking errors. On the other hand, trading volume, lagged ETF price volatility, and broad market volatility may result in poorer ETF tracking performance.

We investigate the determinants of different traders’ trading positions in the gold futures market. With a threshold value determined endogenously by our model, we find that when the gold futures price falls below the threshold, money managers adopt positive feedback trading strategies while swap dealers adopt negative feedback trading strategies. When the futures price rises above the threshold, money managers turn to negative feedback trading and swap dealers reduce the intensity of their negative feedback. In addition, money managers and swap dealers play the transmitter role in trading spillovers to other traders, and their trading transmitter role weakens during periods with high gold prices.

High frequency crude oil option data is used to extract the higher order risk-neutral moments from the crude oil market. These risk-neutral moments include the variance, third central moment and the recently developed tail risk variation measures. We find it is beneficial to disaggregate these risk-neutral moments into their semi-moments, and to work with their log differences instead of the level. The log differences of the second and third semi-moments, and to a lesser extent, the log differences of the tail risk measures, are found to explain returns in the crude oil and S&P 500 futures at high frequency. We also provide evidence that the efficient market hypothesis holds at high frequency in these markets.

This study provides a literature review of academic research related to liquefied natural gas (LNG) hubs development and market integration. Studies show that Asian markets lack a transparent pricing benchmark which exists in North American and European markets. As a result, the formation of functional LNG market hubs in the Asia Pacific region will take time. Early research evidence suggests a strongly cointegrated relationship between LNG and crude oil. Concurring with more recent findings, we confirm that LNG's statistical relationship to both WTI and Brent ceases after the break dates of August 2008 and October 2015, respectively. Multiple initiatives are underway to facilitate development and price discovery for global LNG markets. However, the conclusions found within prior literature are dependent upon the sophistication of the estimation model and sample ranges employed.

This paper proposes a flexible framework for structuring and pricing parametric heat wave insurance. The framework is based on a general heat wave definition formulated in terms of an underlying temperature index. The definition can be varied in terms of the heat wave duration, intensity, measurement period and underlying index. This construction makes it straightforward to create contracts tailored to insure against heat events of many different types. A single stochastic model for the underlying index can be used to price all contracts. We consider contracts with payments that depend on the number of heat waves of a certain type occurring in the measurement period and derive the necessary pricing relations based on a general model structure encompassing several popular temperature models in the literature. An empirical case study is performed using data for Berlin where the daily maximum temperature is used as the underlying index. Model implied heat wave probabilities are consistent with historical patterns, point to high likelihoods for short duration heat events of different threshold temperatures and non-negligible risks for future heat waves of extreme temperatures and durations never before observed.

We find out-of-sample predictability of commodity futures excess returns using combination forecasts of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean–variance investor. Commodity return forecasts are closely linked to the real economy. Return predictability is countercyclical, and the combination forecasts of commodity returns have significant predictive power for future economic activity. Two-factor models featuring the market factor and the innovations in each of the combination forecasts explain a substantial proportion of the cross-sectional variation of both commodity and equity returns. The associated positive risk premiums are consistent with  Merton’s (1973) intertemporal capital asset pricing model (ICAPM), given how the combination forecasts predict an increase in future economic activity and a decline in stock market volatility in the time-series. Overall, combination forecasts act as state variables within the ICAPM, thus resurrecting a central role for macroeconomic risk in determining expected returns on commodities.