Commodity Returns Research Articles

Weathering market swings: Does climate risk matter for agricultural commodity price predictability?

The challenges posed by climate change on the agricultural market have become a pressing concern. An accurate reading of future agricultural commodity prices can be an invaluable planning instrument for diverse interested parties. Here, we explore asset pricing implications of climate risk for the agricultural commodity market from January 2005 to December 2021. Through introducing a composite climate risk index based on the four individual climate risk measures of Faccini et al. (2023), our findings provide valuable insights into the time-series predictability of aggregate climate risk on future agricultural commodity returns, both in- and out-of-sample. This powerful predictability conveys substantial economic benefits to mean–variance investors and cannot be subsumed by conventional economic predictor variables. The evidence further suggests that physical risk, especially global warming, exhibits much stronger return predictability than transition risk. Moreover, we emphasize the pivotal role of climate risk in shaping supply dynamics and capturing investor attention, thereby serving as potential drivers of return predictability. Overall, these predictive insights hold important implications for risk management, investment strategies, and policy formulation in the agricultural commodity market.

The Impact of COVID-19 on Commodity and S&P 500 Sector Return Volatility

We investigate the impact of COVID-19 on commodity return volatility. We find that the impact of COVID-19 on return volatility is different across different markets. Unlike S&P 500 sector indices, commodity return volatility is less sensitive to the impact of COVID-19. The impact of vaccination programs on return volatility is weak for both commodity and financial markets. We employ Fama-French 3 Factor Model and APARCH (1,1) for return volatility estimation. The variation in COVID-19’s impact across different markets has an important implication for return volatility hedging.

ANALYSIS OF THE DEPENDENCIES COMMODITY PRICES AND STOCK MARKET INDEXES USING COPULA

Indonesia is rich in natural resources and occupies an important position in the global raw materials market. The country's rich resources such as oil, coal, nickel, and crude palm oil (CPO) have a significant impact on the economic situation. As one of the world's leading producers and exporters of these raw materials, Indonesia's economic fate is closely linked to price fluctuations. This study uses the copula method to model the dependence between stock and commodity returns and calculates the dependence between commodity prices (oil, coal, nickel, CPO) and Indonesian stock market index (IHSG) The data used for this analysis was sourced from Bloomberg.com, covering the period from 29 September 2021 to 29 September 2023. This study investigates the dynamic dependencies between commodity price returns and the Indonesian stock market index. The results show that the correlations between oil prices and the Indonesian stock index, and between CPO prices and the stock index are generally weak. However, there are exceptions to stock index returns, such as their relatively high dependence on coal and nickel. This diverse research provides valuable insight into the complex interdependencies in Indonesia's financial landscape. Understanding dependence between commodity prices and stock indexes is of great value to investors and policymakers, as it is the basis for making informed decisions to navigate the complex global economy.

Investors’ attention and network spillover for commodity market forecasting

This paper explores the role of network spillovers in commodity market forecasting and proposes a novel factor-augmented dynamic network model. We focus on a novel network definition based on investors’ attention to commodities, positing that commodities exhibit spillovers if they share a similar level of interest. To this aim, we employ Google Trends search data as an instrumental measure for attention. The results reveal that including attention-driven spillovers significantly enhances the forecasting accuracy of commodity returns.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

CLIMATE RISKS AND PREDICTABILITY OF COMMODITY RETURNS AND VOLATILITY: EVIDENCE FROM OVER 750 YEARS OF DATA

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.

Commodity premia and risk management

AbstractWe examine the role of risk management in the context of commodity factor premia. Stopping losses in individual commodities effectively improves the average returns of long‐short commodity premia through persistent reduction in the frequency and severity of drawdowns. The magnitude of improvement is related to the quality of the signal, commodity return volatility, and autocorrelations, as well as transaction costs. The efficacy of a stop‐loss strategy can be enhanced by dynamically calibrating loss thresholds in accordance with realized volatility, and it performs best in high conviction weighting schemes. Overall, we highlight the pivotal role of risk management beyond volatility targeting and risk‐parity in harnessing commodity risk premia.

Commodity returns co-movement, uncertainty shocks, and the US dollar exchange rate

This study examines the time-varying effects of uncertainty shocks identified using the external instrument method on the broad-based movement of commodity returns since the early 1990s. We employ a vector autoregression augmented dynamic factor model with time-varying parameters and stochastic volatility to extract a common factor from 43 commodity returns. We find that uncertainty shocks reduce commodity returns across the board through this common factor and that their effects vary significantly over time, with a tendency to grow much stronger during recessions. Furthermore, uncertainty shocks often lead to dollar appreciation, so they can potentially account for the seemingly negative correlation between commodity returns and strength of the US dollar.

Volatility and dependence in crude oil and agricultural commodity markets

ABSTRACT This paper studies the evolution, volatility dynamics, and nonlinear dynamics between crude oil prices and major agricultural commodity prices. Semiparametric GARCH-in-Mean copula models are applied to monthly crude oil, corn, soybean, palm oil, rice, sugar, and wheat prices from January 1990 to May 2023. We find that the Clayton copula is the best copula to describe the (bivariate) dependence structures between the crude oil and soybean, crude oil and palm oil, crude oil and sugar, and crude oil and wheat markets, suggesting the statistically significant lower tail dependence between crude oil and major agricultural commodity returns. Moreover, the tail dependence is strongest between crude oil and palm oil.

Financial Market Stress and Commodity Returns: A Dynamic Approach

This paper examines the relationship between commodity index returns and the Office of Financial Research Financial Stress Index (OFR FSI). Utilizing the S&P GSCI and its five sub-indices (agriculture, livestock, energy, industrial metals, and precious metals), we find that the causal relationship between financial market stress and commodity index returns is conditional on the sample period examined and the methodology employed. We also note that stress in financial markets has a negative relationship with commodity index returns during low commodity return states; however, during high commodity return states, financial market stress exhibits a positive relationship with commodity index returns. Our findings highlight the importance of considering a time-varying framework for analyzing commodity return dynamics.

The impact of deviations from soybean product crushing estimates on return and risk

AbstractThis study explores how differences in market yields of soymeal and soyoil, as derived from market prices compared to those obtained through the physical process of crushing soybeans, impact the returns and volatilities of these commodities. Positive (negative) deviations are associated with negative (positive) changes in return. Additionally, these deviations positively correlate with return volatility, suggesting that variations from the expected yields lead to price shifts and heightened return volatility. In summary, deviations from the crushing estimates significantly shape the modeling of soybean commodity returns and volatilities, with implications for understanding returns, hedging ratios, and portfolio return variance.

On Commodity Traded Factors and the Cross-Section of Commodity Returns

On Commodity Traded Factors and the Cross-Section of Commodity Returns

Commodity prices under the threat of operational disruptions: Labor strikes at copper mines

The threat of short-term supply disruptions may matter for commodity prices, although their magnitude is hard to detect, for example due to anticipation, storage and to the relatively short duration of disruption events. This article explores global commodity returns for copper around labor strikes in Chile mines between 1910 and 2010. In the five days around strikes, copper display cumulative abnormal returns (CAR) close to 200 basis points (bps). Consistent with the threat of supply disruptions, the effect comes almost fully from strikes at larger mines (CAR≈ 500 bps). Moreover, the price-increasing effect of strikes is stronger when copper inventories are scarce, as measured by the interest-adjusted basis. Despite strikes being transitory events, we also find a mirroring appreciation of the USD/CLP commodity currency.

“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone

This paper presents robust evidence indicating that the tone of financial reports from the US mining industry firms can predict certain mining commodity returns. We assess this predictive ability with different tests, including evaluations of mean squared prediction errors, correlations, mean directional accuracy, and trading strategies. We compared our results with several benchmarks proposed in the literature, including the random walk, outperforming most statistically and economically. Our findings are related to the literature on firms' strategic disclosure choices, suggesting that firms may strategically tailor their disclosure decisions to influence investor expectations regarding future performance. Additionally, we conduct a series of placebo tests, revealing significant outcomes: (1) the tone of financial reports from other industries does not exhibit predictive power for mining industry commodity returns, (2) this predictive ability is not overshadowed when controlling for uncertainty measures, such as VIX, OVX, realized volatility and Google Trends commodity price searches, and (3) the tone of the mining industry's financial report does not predict returns for unrelated mining commodities. To our knowledge, our work is the first paper to employ financial report tone through text mining of nearly 60,000 financial reports to forecast commodity returns. Our findings substantially contribute to the commodity forecasting literature, offering valuable insights for portfolio managers and professionals seeking to enhance their forecasting capabilities.

Climate change economics and the determinants of carbon emissions’ futures returns: A regime-driven ARDL model

This paper employs a Markov Switching Regime (MSR) ARDL model to investigate the determinants of future carbon emission returns, considering distinct regimes of mean and volatility. Utilizing daily data from January 16, 2009, to July 1, 2021, we analyze carbon emissions allowances and returns of major energy commodities. We identify two persistent regimes: high returns with low volatility and low returns with increased volatility. Fluctuations in oil prices significantly influence carbon emissions prices during specific states. Impulse Response Functions (IRFs) reveal short-term fluctuations in carbon emissions returns due to shocks in energy commodity returns, with varying effects between the regimes.

Conditional autoencoder pricing model for energy commodities

We propose a conditional latent factor asset pricing model for energy commodities (CAE) that uses a modified conditional autoencoder neural network to capture the non-linear relationship between latent factors and factor loadings. In addition to spot prices, we incorporate 127 macroeconomic and 598 energy information characteristics to extract the factor loadings. The empirical results demonstrate the high-quality performance of the model in out-of-sample testing. Furthermore, by analyzing characteristic importance, we find that energy information characteristics, particularly coal, electricity, and crude oil and natural gas resource development, play a dominant role in explaining the excess returns of energy commodities.

Geopolitical risk and commodity future returns: Fresh insights from dynamic copula conditional value-at-risk approach

In this study, we investigated the effects of geopolitical risk (GPR) on commodity future returns by computing the conditional value at risk (CoVaR) and delta CoVaR using time-varying and static bivariate copula models. Our results indicate that there is generally a positive dependence between commodity returns and changes in GPR. Empirical evidence also suggests that there are only upside risk spillovers from GPR to commodity markets. The delta CoVaR results locate the greatest systemic risk in heating oil and maize commodities. Future research avenues, as well as policy and practical implications, are outlined.

On the topology of cryptocurrency markets

Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.

The Impact of the Outbreak of Russia-Ukraine War on Commodity, Stock and Cryptocurrency Markets

Purpose: This paper analyzes the impact of the Russian invasion of Ukraine in February 2022 on returns of three groups of assets, i.e., commodities, stocks, and cryptocurrencies. Methodology: The study was conducted using the event study method which allows for quantifying the reaction of market participants to releases of various types of information. Findings: The cumulative abnormal returns (CARs) suggest a mostly positive effect of the conflict outbreak on returns of several commodities, especially precious metals. The obtained results suggest that in times of global crises, investors may consider precious metals as a safe haven. The study also indicates that on the event day the examined stock markets reacted negatively to information about the war, but to varying degrees. The Russian aggression against Ukraine did not affect the cryptocurrency markets in a statistically significant manner. Research limitations: The future studies related to the issue of the impact of Russian aggression against Ukraine on different markets may utilize larger research samples. They also may look for some factors affecting the reaction of markets to information related to the Russian military aggression, like the size of markets, trading volume, or geographical proximity, and economic dependence in the case of equity markets. Value: The study may provide some practical implications for both investors and regulators, especially in relation to the expected behavior of the markets and their informational efficiency in times of global crisis.

Quantile Dependence between Crude Oil and China’s Biofuel Feedstock Commodity Market

This paper investigates the heterogeneous dependence between global crude oil futures and China’s biofuel feedstock commodities under different market conditions. Quantile-on-quantile regression and the causality-in-quantiles test are employed to capture comprehensive and informative relationships. The empirical results are as follows: First, there is a positive relationship between the returns on China’s biofuel feedstock commodities and crude oil. The effects are heterogeneous, conditional on the market regimes, where the impacts of the bearish/bullish crude oil market on biofuel feedstock commodity returns are significant when the commodity market in China is in a bearish/bullish state. Second, crude oil returns have reliable predictive power for the returns on China’s biofuel feedstock commodities under the average market condition and move in connection with the volatility of China’s biofuel-related commodity market in normal and bullish market conditions. Third, the risk reduction effectiveness of soybean and corn is significant, while for wheat, this reduction in portfolio risk is less apparent and enhanced, and the risk reduction effectiveness increases significantly during financial and oil crises. Overall, our findings will be helpful in understanding the heterogeneous interplay between global oil and China’s biofuel-related commodities and in evaluating portfolio diversification opportunities under different market conditions.