Volatile Commodity Research Articles

Does the SDR stabilize investing in commodities?

This paper investigates the potential stabilizing power of the Special Drawing Rights (SDR) on global commodity markets. Using the VAR-BEKK-GRACH, we investigate the impacts of representative shocks from financial market, real economy and foreign exchange market on 25 commodities. Through comparing the volatility spillover on the US dollar-based and SDR-based commodity prices, we find that SDR pricing plays a significant role in stabilizing the prices volatility of energy, metal and agricultural commodities in response to shocks from S&P 500, Baltic Dry Index (BDI) and USDX, respectively. The stability effect on commodity prices varies across specific shocks and commodity varieties. More importantly, by conducting a hedging portfolio exercise in commodity market we show a dominant hedging effectiveness by SDR-denominated bonds to commodities investment compared with the US notes in all cases, except for metals facing S&P 500 shock. The results suggest an effective technique to hedge against commodity market volatilities by combining the SDR-denominated instrument.

Revisiting volatility in global natural resources commodities? Evidence from global data

Natural resource commodities are considered an important factor for economic growth and development. However, volatility in these commodities is a topic of interest, which currently has the attention of scholars. In this regard, the current study investigates volatility in global natural resource commodities while undertaking the Covid-19 pandemic. This study used coal rents (CR), forest rents (FR), mineral rents (MR), natural gas rents (NGR), oil rents (OR), and total natural resource rents (TNRR) to comprehensively measure volatility in natural resource commodities during the period from 1971 to 2020. For empirical investigation of volatility, this study employed autoregressive conditional heteroscedasticity (ARCH) specification, which indicates that CR, FR, MR, and NGR hold no volatility throughout the study period. However, OR and TNRR are found to be volatile throughout the period and during the Covid-19 pandemic. Besides, the generalized threshold ARCH (TGARCH) and exponential generalized ARCH (EGARCH) provide no evidence of positive-negative shocks asymmetry. Also, the results do not provide evidence that negative shock enhances volatility in natural resource commodities more than that of positive shock having the same magnitude. Based on the empirical findings, this study recommends some policy implications in the end.

Spillovers among energy commodities and the Russian stock market

We examine the connectedness in the energy commodities sector and the Russian stock market over the period 2005–2020 using the variance decomposition approach. Our analysis identifies the booms and busts in the correspondence of political and war episodes that are related to spillover effects in the Russian economy, as well as the energy commodities markets. Our findings show that the Russian Oil & Gas and Metals & Mining sectors are net shock contributors of crude oil and have the highest spillovers to other Russian sectors. Furthermore, we disentangle the sources of spillovers that originated from the financial and energy commodity markets and find that a positive change in the energy commodity volatility spillover is associated with an increase in Russian geopolitical uncertainty. Finally, we show that the spread of COVID-19 increases the stock market volatility spillover, whereas it lowers the energy commodity volatility spillover.

The Effect of Export Commodities Price Fluctuation on the Banking Sectors' Financial Performance of the Sub-Saharan African States

This study examines the impact of export commodity price fluctuation on the financial sector performance in some selected sub-Saharan African (SSA) countries. The objective is to determine how instability in export commodity prices transmit to banking sector performance indicators in the region. Essentially a longitudinal study is conducted by combing annual time series data (1996 – 2018) for 18 sub-Saharan Africa countries that are resource exporter sourced from United Nations Conference on Trade and Development (UNCTAD) and World Bank database. The study adopts inferential, descriptive ex-post facto research design, using panel data covering the period 1996 to 2018 sourced from eighteen major primary commodity exporting countries in the sub-Saharan African states. Z-score to evaluate the probability of banking default. Oil price volatility is estimated using a GARCH data analysis framework in terms of mild volatility and intense volatility was applied to estimate the export commodity volatility while Z-score was used to profile the banking performance. It is found that intense commodity price fluctuations exert more debilitating effects on all banking sector performance indicators among the SSA countries with strongest effect of commodity price volatility on the banking sector performance being driven due to vulnerability of the banking sector. It is therefore recommended that banks evolve more efficient resilience mechanisms and that monetary and fiscal policy authorities should seek to enhance domestic capacity of the financial system to withstand domestic export commodity shock over time.

Forecasting volatility in commodity markets with long-memory models

Commodities are the most volatile markets, and forecasting their volatility is an issue of paramount importance. We examine the dynamics of commodity markets volatility by employing three typical long-memory models: fractional integrated generalized autoregressive conditional heteroscedastic (FIGARCH), fractional stochastic volatility (FSV), and heterogeneous autoregressive (HAR) models. Based on a high-frequency futures price dataset of 22 commodities, we confirm that the volatility of commodity markets is rough, and volatility components over different horizons are economically and statistically significant. Long memory with anti-persistence is evident across all commodities, with weekly volatility dominating in most commodity markets and daily volatility for oil and gold markets. HAR models display a clear advantage in forecasting performance compared to the two other models for short horizons, while fractional volatility models yield comparative better forecasts for longer horizons.

EXPERIENCE SHOCKS OF STRATEGIC FOOD CONSUMERS IN INDONESIA DURING COVID-19 PANDEMIC

COVID-19's negative externalities disrupted the supply of several commodities. Meanwhile, to combat the spread of COVID-19, various countries have implemented a lockdown policy, which impacts the economy's balance. Consumers' risks are increasing due to economic changes caused by the COVID-19 pandemic, which affects the higher level of volatility of various agricultural commodities in Indonesia, particularly strategic commodities. Thus, this research was designed to examine the experience shock of strategic food consumers during Covid-19 pandemic. The ARCH/GARCH model was used to describe the strategic food price movement pattern using a set of weekly consumer price data from March 2020 until August 2021. This study found price volatility of strategic food commodities is classified into three categories. For starters, the extreme volatility of commodities such as garlic and cayenne pepper indicates that future buyers will face more significant uncertainty and risk. Furthermore, these commodities will generate fluctuating price swings in the future, resulting in greater experience shock for consumers. Second, commodities with high price volatility, such as rice, chicken meat, eggs, shallots, and sugar, signal that these commodities will subject consumers to a huge shock due to their high price volatility. Third, minimal volatility, such as that found in meat, red chilli, and cooking oil, indicates that customers will face less uncertainty in the future.

Reduction of the Economic Risk by Adaptation Measures to Alleviate Heat Stress in Confined Buildings for Growing-Fattening Pigs Modelled by a Projection for Central Europe in 2030

Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers such as increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulate investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures (AMs) to reduce heat stress. A simulation model driven by meteorological data was used to calculate indoor climate for 1981 to 2017. This dataset was extrapolated for a projection in the year 2030. The heat stress was calculated for a business-as-usual livestock building and for several AMs. For 2030, the expected value of the reduction of the gross margin was calculated by EUR 3.98 a−1 per animal place for the business-as-usual scenario. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980 between EUR 0.03 a−1 and EUR 1.02 a−1 per animal place. The efficiency of other AMs to reduce heat stress is distinctly lower (between EUR 2.62 a−1 and EUR 3.80 a−1 per animal place). The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.

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.

АНАЛІЗ ТОРГІВЛІ ДЕРИВАТИВНИМИ КОНТРАКТАМИ НА МІЖНАРОДНИХ БІРЖАХ

The article highlights the organizational principles of operations on international exchanges in modern conditions of deepening stock exchange consolidation and competition between existing stock exchange alliances. It was established that the global influence on the increase in price volatility of international and national commodity and financial markets became especially noticeable as a result of the war in Ukraine. Today, the prices of many types of commodity and financial assets that are traded on the world's leading stock exchanges have become extremely volatile. Derivative contracts still serve as the main tool in ensuring the minimization of price risks in the conditions of modern uncertainties and risks. It was noted that the organization of derivative contracts exchange trading requires the presence of a well-coordinated exchange infrastructure, in particular, an electronic exchange platform and a settlement and clearing mechanism. The theoretical essence of the exchange derivative contract is highlighted, as well as the classification of the types of derivative contracts on the exchange and over-the-counter markets. Proposed a theoretical definition of exchange-traded derivative contract as standardized term contracts concluded on the exchange and providing for obligations or the right to buy and sell underlying assets in the future. Currently, two types of derivative contracts are most actively traded on exchange platforms: futures and options. The current state of international trade in exchange derivative contracts is analyzed. An analysis of the derivative contracts trade evaluation on the leading commodity and financial exchanges indicates an acceleration of the growth rate in 2021. Thus, according to the statistical reports of the Futures Industry Association, a new number of trade volumes was recorded on derivative contracts international exchanges in 2021, which amounted to 62.58 billion transactions, which increased by a third compared to last year. The main trends in the international exchange market of derivative contracts are established and the main directions of the development of modern exchange trading in commodity and financial derivative contracts in the conditions of increasing level of influence of globalization are highlighted.

THE EFFECT OF COVID-19 ON THE INSURANCE SECTOR OF EMERGING MARKET COUNTRIES

The fear and uncertainty that arose during the COVID-19 pandemic caused an increase in volatility in financial and commodity markets. This study is conducted to show the impact of the number of COVID-19 cases on the insurance sector of developing countries. In the period between the first case dates and March 23, 2021, the relationship between the increase in the number of cases and the increase in the insurance sector index or company return is examined in the selected countries. According to the results, a negative relationship is revealed in all countries except Greece and Hungary, which is not significant.

Dragoslav Drag Avramovic: Probing the limits of development and stabilization policy

Dragoslav Avramovic was one of most accomplished economists in the area of applied development policy. He came from a small country with limited exposure to western economics, and made a stellar career at the World Bank and the Brandt Commission. His main achievements include: (i) A novel approach to external debt management based on dynamic assessment of indebtedness and borrowing capacity. It differed from standard debt-to-GDP ratio approach since it evaluated the efficiency and effectiveness of borrowed resources. (ii) Model for reducing volatility of commodity prices based on intervention fund (buffer stock) activated on either demand or supply side to prevent excessive price changes. (iii) Stabilizing record hyperinflation in his home country Serbia by introducing new currency, sound macro-monetary policy, and breaking inflationary expectations that reduced inflation and allowed price liberalization. He faced formidable political economy opposition to implementing debt management and commodity stabilization models, and structural reforms once the stabilization results were achieved.

Is stock market in Sub-Saharan Africa resilient to health shocks?

PurposeThis paper aims to examine the impact of health and other exogenous shocks on stock markets in Africa. Particularly, the authors examined the resilience of the major stock markets in 12 African economies during the recent global pandemic.Design/methodology/approachThis paper uses the recent panel vector autoregressive model, which enables us to capture the response of stock markets to shocks in COVID-19, commodity markets and exchange rate. For robustness, the authors also analysed the panel Granger causality test. Data was obtained for the period ranging from 2 January 2020 to 31 December 2020.FindingsThe results show that the growth in COVID-19 cases and deaths do not have any substantial impact on the stock market returns of these economies. In terms of commodity markets, the authors find that gold price has a negative contemporaneous effect on stock returns, but the effect fizzles out around the fifth day while crude oil price, on the other hand, has a significant positive simult aneous impact on stock returns and also converges around the fifth day. The authors further find that the exchange rate has a contemporaneous and nonlinear effect on stock returns and seems to be more dramatic when compared with the other variables. Overall, the results show that stock markets in Africa appear to be flexible and resilient against the COVID-19 outbreak but are affected by other exogenous shocks such as volatile commodity prices and the foreign exchange market. The effect is, however, short-lived – between one to five days.Practical implicationsFollowing the study’s findings, policies should be put in place to support financial markets by way of hedging against commodity instability and securing domestic currency financing. Policymakers are also recommended to concentrate on managing the uncertainties around their exchange rate markets and develop robust and efficient domestic financial markets to encourage local and foreign investors.Originality/valueSeveral studies have been carried out on the effects of disasters (such as the COVID-19 pandemic) on stock markets, but only a few studies have examined the resilience of stock markets to health and other exogenous shocks. This study’s attempt is not only to examine the impact of COVID-19 health shocks on stock markets but also to analyse the resilience of the sampled stock markets. The authors also analyse the resilience of stock markets to commodity markets and exchange rates shocks.

Innovation and productivity: the case of Nordic countries

Denmark, Finland, Iceland, Norway, and Sweden form a group of the Nordic countries, considered welfare states and the global leaders in innovative development. The link between the growth rate of total factor productivity and innovative development, controlling such factors as trade openness and prices volatility in commodity markets, is examined. The econometric analysis results show that the relationship between the level of innovative development and the rate of productivity growth is more robust for the Nordic countries than for the EU countries on average. Features of the national innovation systems and financing of RD in the Nordic countries and factors contributing to the impact of innovation activity on productivity are highlighted.

Long-run reversal in commodity returns: Insights from seven centuries of evidence

We perform the longest study of long-run reversal in commodity returns. Using a unique dataset of 52 agricultural, industrial, and energy commodities, we examine the price behavior for the years 1265 to 2017. The findings reveal a strong and robust long-run reversal effect. The returns of the past one to three years negatively predict subsequent performance in the cross-section of returns. The effect is robust to extensive subsample and subperiod analysis, and not driven by statistical biases, extreme events, or macroeconomic risks. Our findings support the explanation that the long-term reversal originates from supply and demand adjustments following price changes. Finally, the phenomenon is elevated in more volatile commodities and in periods of high return dispersion.

Volatility and Depth in Commodity and FX Futures Markets

Prior theory suggests a positive relation between volatility and market depth, while past empirical research finds contrasting results. This paper examines the relation between the volatility and the limit order book depth in commodity and foreign exchange futures markets during a turbulent time using the generalized method of moments (GMM). Results indicate a negative relation between volatility and depth and suggest that the depth in the limit order book decreases as volatility increases. Findings help to understand how market participants provide liquidity in response to shifts in prices.

Volatility spillovers in commodity futures markets: A network approach

AbstractThis study examines the volatility connectedness of commodity futures markets by decomposing connectedness at high, medium, and low frequencies using a LASSO‐VAR model. The total volatility connectedness across commodities is substantial and fluctuates within the range of 30%–60%, which is largely driven by low‐frequency volatility connectedness. Energy futures as a group and crude oil and soybeans as individual commodities play dominant roles as net senders of volatility shocks in the system of 25 commodities. Commodity volatility spillovers at medium and particularly low frequency account for over 40% of the total volatility connectedness, which can be significantly explained by economic factors related to broad economic conditions; however, this is not the case at high frequency. This finding implies that the effect of the financialization of commodities might be more limited than previously assumed.

A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.

Обоснование стратегии развития горнодобывающих предприятий на основе анализа доходности и риска при аутсорсинге и диверсификации

The article presents an approach to addressing the challenges of increasing the efficiency and viability of mining operations in changing market conditions. A solution to the problem posed is based on analyzing the application of various strategies to manage production resources and the overall complex activities of the company depending on the objectives pursued by the user of mineral resources. A mining company can operate in the traditional way, when all production functions and processes are carried out in-house, and the product is only the mined raw material. Outsourcing and production diversification are considered as alternative strategies. The paper describes possible sets and combinations of business strategies, as well as directions to diversify mining companies, including those that involve the formation and development of man-made georesources. A model is described that helps to choose an optimal complex of production functions for a mining company as well as a combination of strategies to be applied, which are balanced in terms of profitability and financial risks of separate types of activity. An economic and mathematical model for optimization of complex activities of the company is presented with the view to minimize risks at a given profitability level and to achieve the maximum efficiency at a given risk level. The research uses a deterministic approach to assess the performance indicators. The model presented has been tested in a small-scale operation that mines chromium ores. The research shows that a diversification strategy for a mining company reduces the risk of economic losses and increases its viability in conditions of volatile commodity prices.

Maize price volatility and deforestation

AbstractWe develop a model of deforestation using a real option on agricultural land conversion and establish an important link between agricultural commodity price volatility and forest loss. Higher price volatility of staple commodities increases incentives to delay land conversion and is associated with lower levels of deforestation. Using satellite‐derived estimates of local deforestation and high‐frequency local maize prices across a panel of market catchments across 26 countries in sub‐Saharan Africa, we find strong empirical evidence that maize price volatility is negatively associated with forest loss. Our findings are driven by catchments in biomes that are relatively well‐suited to maize production. Instrumenting for changes in price levels and price volatility with spatially explicit temperature and rainfall anomalies yields results consistent with our main approach. The findings highlight important tensions between agricultural price stabilization policies, conservation efforts, and environmental externalities.

Dynamic efficiency of China’s commodity futures market through the lens of high frequency data

In this paper, we use the permutation entropy algorithm to derive the static and dynamic permutation entropy of commodity futures, and to evaluate the effectiveness of main products in China’s commodity futures market. The intraday data of six varieties belonging to six categories in China’s commodity futures market are taken as samples. We find the following: (1) The return distribution of the main varieties shows high peaks, fat tails and asymmetry, and follows the biased random walk distribution characteristics; (2) The permutation entropy of all varieties decreases significantly in the same time window, during which the price volatility of major commodity markets rises. And the time window coincides with the impact time of COVID-19 epidemic; (3) By comparing the distribution of permutation entropy of main varieties in different stages of event shock, we found that the mean value of permutation entropy decreases significantly during the process of event shock, and the price fluctuates greatly. Therefore, the significant decrease of permutation entropy is a valuable warning signal for regulators and investors.