Showing papers on "Commodity market published in 2021"

A shock like no other: coronavirus rattles commodity markets.

Abstract: The novel coronavirus (2019-nCoV) originated in China has now covered around 213 countries globally. It has posed health calamities which have threatened the world with the emergence. Owing to the number of confirmed cases still rising every day, it has now become a phase of an international health emergency. Sudden outbreak of coronavirus disease 2019 (COVID-19) has brought global declines in the commodity process. This has majorly affected the demand as well as supply of the commodities. The oil market has been severely affected due to the outrageous collapse in the demand majorly due to travel restrictions which has also caused the steepest decline in oil prices. The prices of both precious and industrial metals have also fallen, although the price drop is less than that of oil prices. The agriculture industry is one of the least affected so far by this pandemic due to its indirect relation with economic activities. However, the ultimate impact of COVID-19 pandemic will greatly depend on the severity and duration of its outspread, but it is expected to have long-lasting implications.

Commodity Derivatives: Markets and Applications

Abstract: Preface. Acknowledgements. About the Author. 1 An Introduction to Derivative Products. 1.1 Forwards and futures. 1.2 Swaps. 1.3 Options. 1.4 Derivative pricing. 1.4.1 Relative Value. 1.5 The spot-forward relationship. 1.5.1 Deriving forward prices: market in contango. 1.5.2 Deriving forward prices: market in backwardation. 1.6 The spot-forward-swap relationship. 1.7 The spot-forward-option relationship. 1.8 Put-call parity: a key relationship. 1.9 Sources of value in a hedge. 1.10 Measures of option risk management. 1.10.1 Delta. 1.10.2 Gamma. 1.10.3 Theta. 1.10.4 Vega. 2 Risk Management. 2.1 Categories of risk. 2.1.1 Defining risk. 2.1.2 Credit risk. 2.2 Commodity market participants: the time dimension. 2.2.1 Short-dated maturities. 2.2.2 Medium-dated maturities. 2.2.3 Longer-dated exposures. 2.3 Hedging corporate risk exposures. 2.4 A framework for analysing corporate risk. 2.4.1 Strategic considerations. 2.4.2 Tactical considerations. 2.5 Bank risk management. 2.6 Hedging customer exposures. 2.6.1 Forward risk management. 2.6.2 Swap risk management. 2.6.3 Option risk management. 2.6.4 Correlation risk management. 2.7 View-driven exposures. 2.7.1 Spot-trading strategies. 2.7.2 Forward trading strategies. 2.7.3 Single period physically settled "swaps." 2.7.4 Single or multi-period financially settled swaps. 2.7.5 Option-based trades: trading volatility. 3 Gold. 3.1 The market for gold. 3.1.1 Physical Supply Chain. 3.1.2 Financial Institutions. 3.1.3 The London gold market. 3.1.4 The price of gold. 3.1.5 Fixing the price of gold. 3.2 Gold price drivers. 3.2.1 The supply of gold. 3.2.2 Demand for gold. 3.2.3 The Chinese effect. 3.3 The gold leasing market. 3.4 Applications of derivatives. 3.4.1 Producer strategies. 3.4.2 Central Bank strategies. 4 Base Metals. 4.1 Base metal production. 4.2 Aluminium. 4.3 Copper. 4.4 London metal exchange. 4.4.1 Exchange-traded metal futures. 4.4.2 Exchange-traded metal options. 4.4.3 Contract specification. 4.4.4 Trading. 4.4.5 Clearing. 4.4.6 Delivery. 4.5 Price drivers. 4.6 Structure of market prices. 4.6.1 Description of the forward curve. 4.6.2 Are forward prices predictors of future spot prices? 4.7 Applications of derivatives. 4.7.1 Hedges for aluminium consumers in the automotive sector. 4.8 Forward purchase. 4.8.1 Borrowing and lending in the base metal market. 4.9 Vanilla option strategies. 4.9.1 Synthetic long put. 4.9.2 Selling options to enhance the forward purchase price. 4.9.3 "Three way." 4.9.4 Min-max. 4.9.5 Ratio min-max. 4.9.6 Enhanced risk reversal. 4.10 Structured option solutions. 4.10.1 Knock-out forwards. 4.10.2 Forward plus. 4.10.3 Bonus forward. 4.10.4 Basket options. 5 Crude Oil. 5.1 The value of crude oil. 5.1.1 Basic chemistry of oil. 5.1.2 Density. 5.1.3 Sulphur content. 5.1.4 Flow properties. 5.1.5 Other chemical properties. 5.1.6 Examples of crude oil. 5.2 An overview of the physical supply chain. 5.3 Refining crude oil. 5.3.1 Applications of refined products. 5.4 The demand and supply for crude oil. 5.4.1 Proved oil reserves. 5.4.2 R/P ratio. 5.4.3 Production of crude oil. 5.4.4 Consumption of crude oil. 5.4.5 Demand for refined products. 5.4.6 Oil refining capacity. 5.4.7 Crude oil imports and exports. 5.4.8 Security of supply. 5.5 Price drivers. 5.5.1 Macroeconomic issues. 5.5.2 Supply chain considerations. 5.5.3 Geopolitics. 5.5.4 Analysing the forward curves. 5.6 The price of crude oil. 5.6.1 Defining price. 5.6.2 The evolution of crude oil prices. 5.6.3 Delivered price. 5.6.4 Marker crudes. 5.6.5 Pricing sources. 5.6.6 Pricing methods. 5.6.7 The term structure of oil prices. 5.7 Trading crude oil and refined products. 5.7.1 Overview. 5.7.2 North Sea oil. 5.7.3 US crude oil markets. 5.8 Managing price risk along the supply chain. 5.8.1 Producer hedges. 5.8.2 Refiner hedges. 5.8.3 Consumer hedges. 6 Natural Gas. 6.1 How natural gas is formed. 6.2 Measuring natural gas. 6.3 The physical supply chain. 6.3.1 Production. 6.3.2 Shippers. 6.3.3 Transmission. 6.3.4 Interconnectors. 6.3.5 Storage. 6.3.6 Supply. 6.3.7 Customers. 6.3.8 Financial institutions. 6.4 Deregulation and re-regulation. 6.4.1 The US experience. 6.4.2 The UK experience. 6.4.3 Continental European deregulation. 6.5 The demand and supply for gas. 6.5.1 Relative importance of natural gas. 6.5.2 Consumption of natural gas. 6.5.3 Reserves of natural gas. 6.5.4 Production of natural gas. 6.5.5 Reserve to production ratio. 6.5.6 Exporting natural gas. 6.5.7 Liquefied natural gas. 6.6 Gas price drivers. 6.6.1 Definitions of price. 6.6.2 Supply side price drivers. 6.6.3 Demand side price drivers. 6.6.4 The price of oil. 6.7 Trading physical natural gas. 6.7.1 Motivations for trading natural gas. 6.7.2 Trading locations. 6.7.3 Delivery points. 6.8 Natural gas derivatives. 6.8.1 Trading natural gas in the UK. 6.8.2 On-the-day commodity market. 6.8.3 Exchange-traded futures contracts. 6.8.4 Applications of exchange-traded futures. 6.8.5 Over-the-counter natural gas transactions. 6.8.6 Financial/Cash-settled transactions. 7 Electricity. 7.1 What is electricity? 7.1.1 Conversion of energy sources to electricity. 7.1.2 Primary sources of energy. 7.1.3 Commercial production of electricity. 7.1.4 Measuring electricity. 7.2 The physical supply chain. 7.3 Price drivers of electricity. 7.3.1 Regulation. 7.3.2 Demand for electricity. 7.3.3 Supply of electricity. 7.3.4 Factors influencing spot and forward prices. 7.3.5 Spark and dark spreads. 7.4 Trading electricity. 7.4.1 Overview. 7.4.2 Markets for trading. 7.4.3 Motivations for trading. 7.4.4 Traded volumes: spot markets. 7.4.5 Traded volumes: forward markets. 7.5 Nord pool. 7.5.1 The spot market: Elspot. 7.5.2 Post spot: the balancing market. 7.5.3 The financial market. 7.5.4 Real-time operations. 7.6 United states of america. 7.6.1 Independent System Operators. 7.6.2 Wholesale markets in the USA. 7.7 United kingdom. 7.7.1 Neta. 7.7.2 UK trading conventions. 7.7.3 Load shapes. 7.7.4 Examples of traded products. 7.7.5 Contract volumes. 7.7.6 Contract prices and valuations. 7.8 Electricity derivatives. 7.8.1 Electricity forwards. 7.8.2 Electricity Swaps. 8 Plastics. 8.1 The chemistry of plastic. 8.2 The production of plastic. 8.3 Monomer production. 8.3.1 Crude oil. 8.3.2 Natural gas. 8.4 Polymerisation. 8.5 Applications of plastics. 8.6 Summary of the plastics supply chain. 8.7 Plastic price drivers. 8.8 Applications of derivatives. 8.9 Roles of the futures exchange. 8.9.1 Pricing commercial contracts. 8.9.2 Hedging instruments. 8.9.3 Source of supply/disposal of inventory. 8.10 Option strategies. 9 Coal. 9.1 The basics of coal. 9.2 The demand for and supply of coal. 9.3 Physical supply chain. 9.3.1 Production. 9.3.2 Main participants. 9.4 The price of coal. 9.5 Factors affecting the price of coal. 9.6 Coal derivatives. 9.6.1 Exchange-traded futures. 9.6.2 Over-the-counter solutions. 10 Emissions Trading. 10.1 The science of global warming. 10.1.1 Greenhouse gases. 10.1.2 The carbon cycle. 10.1.3 Feedback loops. 10.2 The consequences of global warming. 10.2.1 The Stern Report. 10.2.2 Fourth assessment report of the IPCC. 10.3 The argument against climate change. 10.4 History of human action against climate change. 10.4.1 Formation of the IPCC. 10.4.2 The Earth Summit. 10.4.3 The Kyoto Protocol. 10.4.4 From Kyoto to Marrakech and beyond. 10.5 Price drivers for emissions markets. 10.6 The EU emissions trading scheme. 10.6.1 Background. 10.6.2 How the scheme works. 10.6.3 Registries and logs. 10.6.4 National Allocation Plans (NAPs). 10.7 Emission derivatives. 11 Agricultural Commodities and Biofuels. 11.1 Agricultural markets. 11.1.1 Physical supply chain. 11.1.2 Sugar. 11.1.3 Wheat. 11.1.4 Corn. 11.2 Ethanol. 11.2.1 What is ethanol? 11.2.2 History of ethanol. 11.3 Price drivers. 11.3.1 Weather. 11.3.2 Substitution. 11.3.3 Investor activity. 11.3.4 Current levels of inventory. 11.3.5 Protectionism. 11.3.6 Health. 11.3.7 Industrialising countries. 11.3.8 Elasticity of supply. 11.3.9 Genetic modification. 11.4 Exchange-traded agricultural and ethanol derivatives. 11.5 Over-the-counter agricultural derivatives. 12 Commodities Within an Investment Portfolio. 12.1 Investor profile. 12.2 Benefits of commodities within a portfolio. 12.2.1 Return enhancement and diversification. 12.2.2 Asset allocation. 12.2.3 Inflation hedge. 12.2.4 Hedge against the US dollar. 12.3 Methods of investing in commodities. 12.3.1 Advantages and disadvantages. 12.4 Commodity indices. 12.4.1 Explaining the roll yield. 12.5 Total return swaps. 12.6 Structured investments. 12.6.1 Gold-linked notes. 12.6.2 Capital guaranteed structures. 12.6.3 Combination structures. 12.6.4 Non-combination structures. 12.6.5 Collateralised Commodity Obligations. 12.7 Analysing investment structures. Glossary. Notes. Bibliography. Index.

Assessing the Impact of COVID-19 Pandemic on the Stock and Commodity Markets Performance and Sustainability: A Comparative Analysis of South Asian Countries

Abstract: COVID-19 is certainly the first sustainability crisis of the 21st century. The paper examines the impact of COVID-19 on the Indian stock and commodity markets during the different phases of lockdown. In addition, the effect of COVID-19 on the Indian stock and commodity markets during the first and second waves of the COVID-19 spread was compared. A comparative analysis of the stock market performances and sustainability of selected South Asian countries is also included in the study, which covers the lockdown period as well as the time frame of the first and second waves of COVID-19 spread. To examine the above relationship, the conventional Welch test, heteroskedastic independent t-test, and the GMM multivariate analysis is employed, on the stock return, gold prices, and oil prices. The findings conclude that during the different phases of lockdown in India, COVID-19 has a negative and significant impact on oil prices and stock market performance. However, in terms of gold prices, the effect is positive and significant. The results of the first wave of COVID-19 infection also corroborate with the above findings. However, the results are contradictory during the second wave of coronavirus infection. Furthermore, the study also substantiates that COVID-19 has significantly affected the stock market performances of selected South Asian countries. However, the impact on the stock market performances was only for a short period and it diminished in the second wave of COVID-19 spread in all the selected South Asian countries. The findings contribute to the research on the stock and commodity market impact of a pandemic by providing empirical evidence that COVID-19 has spill-over effects on stock markets and commodity market performances. This result also helps investors in assessing the trends of the stock and commodity markets during the pandemic outbreak.

Development of copper price from July 1959 and predicted development till the end of year 2022

Abstract: The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators' profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement - e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.

Take Bitcoin into your portfolio: a novel ensemble portfolio optimization framework for broad commodity assets

Abstract: The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio. In this study, we propose a novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation. Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors. In addition, it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies, commodity funds, and indices.

Mining in Africa after the supercycle: New directions and geographies

Abstract: Mining in Africa is at a pivotal moment. For most of the period 2000 to 2012, the extractive industries were in a “supercycle” of sustained high commodity prices. Driven by resource-intensive growth in emerging market economies, these high commodity prices were anticipated to continue for decades to come. However, this “supercycle” ended in 2012 and there followed a severe slump in mineral prices from 2014 onwards. On the one hand, a new era of commodity market dynamics has begun, with changing patterns of economic activity, minerals governance, and environmental regulation. On the other hand, the end of the supercycle has continued or intensified pre-existing trends towards mechanisation, automation, and enclavity, while distributive pressures on companies by local communities and host nations increase. We argue that the end of the supercycle has reconfigured the geographies of extraction in ways that are not yet reflected in existing research or taken into consideration in policy implementation, particularly around corporate strategy, state–business relations, and models for mineral-based development strategies. In this paper we map the terrain of research on the supercycle in Africa and identify emerging post-supercycle trends – some of which have overtaken research. The paper is structured around examining four themes: (1) new geographies of investment and extraction; (2) new geographies of struggle; (3) national minerals-based development; and (4) labour and livelihoods, for which we identify key trends during the supercycle and post-supercycle and areas for future research and policy development.

The Macroeconomic Effects of Commodity Price Uncertainty

Abstract: This paper studies the macroeconomic effects of commodity price uncertainty (CPU) shocks. Using Australia as a case study, an econometric-based CPU index is proposed to reveal that Australia has experienced an unprecedented increase in uncertainty from the commodity market recently. Evidence from a VAR model shows that CPU shocks have a larger recessionary impact than other relevant uncertainty shocks such as financial, economic and trade policy uncertainty. The empirical results are then interpreted in a non-linear multisector DSGE model of the Australian economy by estimating key parameters in the DSGE model to match its responses to the VAR responses. CPU shocks in the DSGE model, via foreign commodity export demand with price rigidity, trigger a precautionary response and cause a decline in real economic activity.

Energy commodity price response to covid-19: Impact of epidemic status, government policy, and stock market volatility

Abstract: The outbreak of the COVID-19 pandemic has hit the global financial markets, including energy commodities. The aim of the paper is to examine the reaction of the energy commodity market to the COVID-19 pandemic, particularly the epidemic status, the stringency of the government antiCOVID-19 policy, and the stock market volatility. We use daily data on the S&P GSCI Energy index, the number of new confirmed COVID-19 global cases, the self-developed Global Stringency Index, and the VIX index. The research covers the period from January 2 to September 30, 2020, i.e. the first phase of the COVID-19 pandemic. Based on a structural vector autoregressive model we observe a significant and negative energy commodity market’s reaction to the changes in the stock market volatility. Moreover, the results imply that the increase in the Global Stringency Index leads to the decline in the S&P GSCI Energy index but the reaction is significant only on the third day after the shock. We reveal no significant impact of global epidemic status on energy commodity prices. © 2021, Econjournals. All rights reserved.

Commodity Market Integration

Abstract: The literature on market integration explores the development of the commodity market with data on prices, which is a useful complement to analysis of trade and the only feasible approach when data on trade are not available. Data on prices and quantity can help in understanding when markets developed, why, and the degree to which their development increased welfare and economic growth. Integration progressed slowly throughout the early modern period, with significant acceleration in the first half of the 19th century. Causes of integration include development of transportation infrastructure, changes in barriers to trade, and short-term shocks, such as wars. Literature on the effects of market integration is limited and strategies for estimating the effects of market integration are must be developed.

Asymmetric Risk Spillover of the International Crude Oil Market in the Perspective of Crude Oil Dual Attributes

Abstract: The heterogeneity of investor sentiment plays the key role in causing the asymmetry of information transmission pattern and transmission intensity between markets. This paper analyzes the asymmetric risk spillover between the international crude oil market and other markets, including commodity market and financial market, using monthly data from June 2006 to October 2020. Risk from the international crude oil market is separated into upside and downside risks. The empirical results suggest that, first, from the perspective of static spillover, the risk spillover between the international oil market and other markets enhances significantly in response to rising return; second, from the perspective of dynamic spillover, the asymmetric risk spillover of international crude oil market manifests the key roles played by important events happened in crude oil market and alternating attributes of crude oil. Some policy suggestions are proposed in light of these empirical results

The economic history of commodity market development

Abstract: Trade and division of labour have been a key source of economic growth throughout human history and they have been widely researched, within the constraints of the available data. This chapter surveys the twin literatures on trade (based on quantity data) and market integration (based on price data). The first section outlines the development of commodity markets in the very long run, as measured by growth of trade and convergence of prices. Then, it discusses the contribution of technical progress in transportation and of changes (downward but also upward) in barriers to trade to these changes. The last section reviews the literature on effects od the development of markets. Scholars have made good progress in estimating the static benefits on welfare but the work on the dynamic effects is just beginning.

Does the risk aversion of crude oil market investors have directional predictability for the precious metal and agricultural markets

Abstract: PurposeRisk aversion is considered as an important factor in predicting asset prices. Many studies have proved that there exists important price information spillover among crude oil, precious metals and agricultural markets. Then there naturally follows the question: Is the risk aversion of investors in crude oil market predictable for the returns of precious metals and agricultural products? The purpose of this paper is to answer this question. For this reason, the authors explore the directional predictability and the cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products.Design/methodology/approachTo better describe the risk aversion of investors, this paper uses high-frequency data and model-free calculation method to obtain variance risk premium of crude oil. Then, this paper uses the cross-quantilogram method to investigate the directional predictability and cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products. Meanwhile, it employs the partial cross-quantilogram (PCQ) method to test the impact of control variables on the empirical results.FindingsFirstly, risk aversion of crude oil market investors has directional predictability for returns of precious metals and agricultural products. Secondly, different degrees of risk aversion of crude oil market investors have different impacts on returns of precious metals and agricultural products. A low (high) degree of crude oil market investors' risk aversion has negative (positive) predictability for returns of precious metals and agricultural products. Finally, during the sample period, the returns of precious metals are more affected by risk aversion of crude oil market investors than returns of agricultural products.Originality/valueFirst of all, this paper studies the impact of risk aversion of crude oil market investors on returns of precious metals and agricultural products. It updates previous relevant studies on the factors influencing the prices of precious metals and agricultural products, and provides a new idea for the forecast of those commodity returns. Secondly, this paper provides the evidence that different degrees of risk aversion of investors have different effects on the returns of commodities, and expands the research on the topic of commodity returns prediction. Finally, high-frequency data are employed in this paper to better capture the risk aversion of investors than commonly used daily data.