Commodity Spot Research Articles

Application of Variance Decomposition Model in Pricing Analysis of Agriculture Commodity Spot and Future Market

Application of Variance Decomposition Model in Pricing Analysis of Agriculture Commodity Spot and Future Market

Tail risk spillover of commodity futures markets

AbstractThis paper examines the tail risk spillover in commodity futures markets, with a particular focus on the dynamics related to the Chinese markets. To overcome the limitations of conventional network methods in terms of dimensionality, we employ a bootstrap‐based probabilistic analysis to extend the Diebold–Yilmaz network model for measuring spillover effects. Our empirical results demonstrate both intra‐ and inter‐group tail risk connectedness among commodity futures, highlighting variations in such connectedness during crisis periods. Additionally, we find the tail risk spillover between commodity spot and futures markets and identify dominant sources of risk transmission through our probabilistic analysis.

Nonlinear price transmission and asynchronous price bubbles: empirical evidence from China’s agricultural futures and spot markets

ABSTRACT Previous studies on commodity price bubbles mainly focused on futures markets and ignored the performance of spot markets. Using the price data for corn and soybeans in China, this study identifies the exact bubble dates for the futures and spot markets, and finds asynchronous price bubbles between these two markets. Bubbles are more frequent for commodity spot prices, while the corresponding futures prices still dominate the process of price discovery. Further analysis reveals that, the lack of (immediate) linear transmission between the cointegrated prices may have inhibited bubble synchronization, and caused more spot price bubbles. The nonlinear transmission effects between the futures and spot prices suggest the existence of speculative storage and market power. This may further explain why spot price bubbles cannot be arbitraged away.

STUDY ON COMMODITY TRADING ANALYSIS AND INVESTOR’S PERCEPTION

Commodity trading is as old as human civilization and one of the earliest economic pursuits of mankind. Over the centuries, commodity trading has evolved from the barter system to spot markets to derivatives markets. In barter system, goods were exchanged between two parties with matching and opposite needs (for example, bags of wheat were exchanged for cattle). Over a period of time, commodities brought from distant places were exchanged for gold and silver. With the introduction of Money as a medium of exchange, there was a paradigm shift in commodity trading with the value of commodity being expressed in monetary terms and trading in commodities was conducted mainly through the medium of currency. Commodity spot markets evolved in many places and the counterparties met at these common places where goods were brought for immediate sale and delivery at the market price decided by the demand and supply forces. In commodity spot markets, traders sell goods such as rice for immediate delivery against cash. At some stage, counterparties started entering into agreements to deliver commodities (e.g.: wheat) at a specified time in future at a price agreed today. These forward contracts, more often than not, were not honored by either of the contracting parties due to price changes and market conditions. A seller pulled out of the contract if the spot price was more profitable for him than the contracted price. A buyer also backed out from executing the contract on maturity if he was able to get the commodity at a cheaper price from the spot market. Futures emerged as an alternative financial product to address

Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach

Most of the academic literature on connectedness focuses on stock markets and commodity spot markets. However, there is still much to say about the connectedness among commodity futures markets at different expiration dates, as this part of the literature is as yet small and inconclusive. This paper builds on the existing literature by focusing on connectedness among a set of ten commodity futures markets (including energy, agriculture, and metal sectors) at different maturities, the global equity market and three different sources of uncertainty (financial, economic, and geopolitical) over the period 2000–2022. In doing so, we estimate a combination of complementary connectedness metrics based on the work of Diebold and Yilmaz (2012), which enables measuring average, and the works of Chatziantoniou et al. (2021) and Ando et al. (2022) which enable measuring connectedness under different market circumstances (i.e., low, median and high quantiles). The analysis provides evidence of the variable aspect of connectedness across commodities and uncertainty measures assessed across different quantiles. The average directional connectedness network suggests that commodity futures markets within the same category are significantly sensitive to each-other. However, interdependencies between commodities belonging to different categories are relatively lower. The average total connectedness across quantiles provides evidence of (i) a clear symmetric pattern at the extreme lower and upper quantiles, and (ii) an increase in connectedness with the magnitude of extreme negative and positive shocks. The time-varying analysis indicates that connectedness increases at all quantiles during periods of high market stress, but with relatively higher intensity at the lower quantiles. Additionally, the structure and magnitude of connectedness at the extremes – upper and lower quantiles – differs from the pattern of connectedness at the median quantiles.

Will commodity futures reduce systemic risk in the spot market? Evidence from Chinese commodity market

PurposeThis paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.Design/methodology/approachUtilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.FindingsThe findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.Originality/valueThe paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

Do spot market auction data help price discovery?

This paper contributes to the price discovery literature by establishing, for the first time, the role of commodity spot market auction data. Using the New Zealand whole milk powder market as an example, we show that auction-level data explain the price discovery dynamics above and beyond determinants previously identified as being relevant to spot and futures market price formation. In particular, the price discovery of the futures market rises with the volume of dairy products traded at the auction, signaling that the volume auctioned induces a change in the trading strategies of futures market participants. The whole milk powder discovery process is found to primarily take place in the spot market, which aligns well with the auction predating the introduction of the futures market, its higher volume, and lower trading costs.

Commodity momentum and reversal: Do they exist, and if so, why?

AbstractQuestions as to why differences in momentum and reversal patterns seem to emerge in commodity futures compared with spot markets, and how these patterns can be explained, remain unanswered. To investigate these questions, I examine 23 commodities over a period of 60 years. I first show that including the net convenience yield in the definition of commodity spot returns reconciles the differences in the results for commodity spot and futures markets. Both commodity futures and spot markets exhibit quantitatively consistent momentum and reversal effects. An initial momentum effect is followed by a reversal effect and then another momentum effect. These observed patterns in commodities can be jointly explained by a combination of traditional asset pricing factors and a basis factor related to the net convenience yield.

Estimating and pricing commodity futures with time‐delay stochastic processes

In commodity futures pricing models, the commodity present price is generally considered to reflect all information in the markets and past information is not regarded important. However, there is some empirical evidence that shows that this fact is unrealistic. In this paper, we consider some stochastic models with delay for pricing commodity futures. The functions of the commodity price stochastic process under the risk‐neutral measure are necessary for pricing derivatives. However, the observations in the market have risk. Then, we use a technique that allows us to estimate the functions of the risk‐neutral commodity spot price stochastic process, directly from futures prices traded in the market, and show how to price the commodity futures. Finally, we make an empirical application of this methodology with gold futures traded in the COMEX. Furthermore, we make clear the supremacy of the delay models in pricing gold futures.

Pricing Asian options with stochastic convenience yield and jumps

We price Asian options on commodity futures contracts in the presence of stochastic convenience yield, stochastic interest rates and jumps in the commodity spot price. In the case of no jumps, we obtain a closed-form solution for a geometric average Asian option. This analytic result enables us to employ this option as a suitable control variate when pricing the corresponding arithmetic average Asian option. Discussion of further applications and comparative statics are presented. To cover the case with jumps, we condition on the jump times first and then average over the sequences of jump times.

Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility

An increasing number of studies have verified the impact of climate change on commodity spot markets. Based on the forecasting framework, we revisit this work by focusing on the crude oil spot markets. We employ the Southern Oscillation Index (SOI) to capture the climate changes as it determines the weather and climate of numerous living areas. To obtain a more accurate result, we apply the STL decomposition to construct a series of the GARCH-MIDAS models combining the trend, seasonal, and remainder components of SOI. The empirical results reveal that the SOI trend show significance in the in-sample estimation and the model involving it outperforms others in the out-of-sample prediction. Hence, the trend of weather and climate changes may provide greater economic value for market participants investing crude oil spot markets.

Correction to: forecasting oil commodity spot price in a data-rich environment

Correction to: forecasting oil commodity spot price in a data-rich environment

Forecasting oil commodity spot price in a data-rich environment.

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.

Commodity Prices after COVID-19: Persistence and Time Trends

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode.

On correlated measurement errors in the Schwartz–Smith two-factor model

Abstract The Schwartz–Smith two-factor model is commonly used for pricing of derivatives in commodity markets. For estimating and forecasting the term structures of futures prices, the logarithm of commodity spot price is represented as the sum of short- and long-term factors being the unobservable state variables. The futures prices derived as functions of the spot price lead to the simultaneous set of measurement equations, which is used for joint estimation of unobservable state variables and the model parameters through a filtering procedure. We propose a modified model where the error terms in the measurement equations are assumed to be serially correlated. In addition, for comparative analysis, the modelling of the logarithmic returns of futures prices is also considered. Out-of-sample prediction performances of two proposed models were illustrated using European Unit Allowances (EUA) futures prices from January 2017 to April 2021. Historically, this period corresponds to the second half of Phase III, and the beginning of Phase IV of the European Union Emission Trading System (EU-ETS).

Implied volatility surface construction for commodity futures options traded in China

European futures options are not traded on the Chinese exchanges and that generates difficulties to calibrate fundamental market parameters, such as the implied volatilities. We propose an efficient willow tree method to resolve the problem of calibrating the implied volatility from American-style options. The proposed willow tree construction is independent of the volatility itself so as to minimize the cost of the calibration. We also apply the proposed method to calibrate the implied volatilities of most frequently traded options in the Chinese market, sugar and soybean meal, based on the daily closing prices, and construct the corresponding implied volatility surfaces. The results indicate the seasonality in the volatility of commodity spot prices and futures prices in China. Moreover, based on the implied volatility distortion close to the option maturity observed in our empirical results, we suggest a minimum tick price scheme to avoid the distortion and decrease of hedging costs.

The dollar’s ”Convenience Yield”

I link deviations from forward-spot parity for currencies and commodities. The key is to think of the U.S. dollar as a “commodity.” When commodity spot prices are too high compared to futures, arbitrageurs will short the commodity and bank dollars. When physical scarcity constrains commodity borrowing, the result is a positive convenience yield. In the currency space, it is the dollar itself that needs to be shorted, with proceeds converted spot and deposited in foreign currency. When dollars are hard to borrow, covered interest parity (CIP) deviations arise. Thus, a negative “cross-currency basis” is the U.S. dollar’s positive convenience yield.

Equilibrium pricing of commodity spot and forward under incomplete markets with implications on convenience yield

Equilibrium pricing of commodity spot and forward under incomplete markets with implications on convenience yield

Modeling and Forecasting Commodity Futures Prices: Decomposition Approach

Price instability is a paramount concern since commodity prices are associated with the livelihood and the economy of a nation as a whole; any extraordinary price fluctuation in the futures market shows that forecasts in commodities is an essential venture. The difficulties in predicting commodity prices are due to the unpredictability of the world’s financial issues, fiscal dispensation, the speculative market’s exacerbation, and several other elements. This study aims to model and forecast the market price of commodity futures. We applied decomposition techniques, empirical mode decomposition (EMD), and variational mode decomposition (VMD) to three commodities: corn, crude oil, and gold over the commodity spot market prices. We used the Granger causality test to establish mutual relationships amongst the three commodity futures prices. Three commodity price data with different periods were decomposed into several intrinsic modes. Using three forecasting performance evaluation criteria, statistical measures such as mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MAPE) to compare the capabilities of the suggested models. We also introduced Diebold Mariano (DM) test in selecting the optimal models for each commodity, since MAE, RMSE and MAPE have some shortcomings. We found that the combined models outperformed the individual back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models in forecasting corn and crude oil futures prices series, while BPNN emerged as the optimal model for predicting gold futures prices series. Variational mode decomposition emerged as the ideal data pre-treatment method and contributed to enhancing the predicting ability of the BPNN and the ARIMA models. The empirical results showed that models combined with decomposition methods predict commodity futures prices accurately and can easily capture the volatility in commodity futures prices.

The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test

The Baltic Dry Index (BDI) is a unique gauge for measuring the marine transportation of major dry bulk shipments. Increased sea freight is a precursor to the increase in economic activities. The volumes of sea trade and freight rates are influenced by import–export dynamics and changes in commodity prices. So, levels of commodity prices are monitored to gain insight into the anticipated demand for bulk shipments. In this study, the causality-in-quantiles (CiQ) model is used to model the causal relationship between BDI spot values and spot price of major dry bulk commodities like iron ore, aluminum, copper, agricultural products by considering 12 years of daily data. CiQ model is superior compared to other linear causality models as it helps in capturing the asymmetry and nonlinearity in causality based on different quantiles or market conditions i.e., bearish, normal, and bullish market conditions. Also, it captures the causality-in-mean as well as variance and helps in exploring the causal relationship in returns as well as volatility transmission between BDI and commodity prices. The finding of the paper throws interesting light on the asymmetric relationship between BDI and commodity prices- commodity prices are causing BDI in all market conditions, but the influence is stronger in normal periods than bearish and bullish periods. The causality from commodity to BDI follows a common pattern across most of the commodities. However, the effect of BDI on commodities considerably varies across the range of commodities and across market conditions. So, this model provides a plethora of information that will help commodity market participants to hedge the risk of variations in commodity price and freight rates effectively.