Soft Commodities Research Articles

Dynamic Returns Connectedness: Portfolio Hedging Implications During the COVID‐19 Pandemic and the Russia–Ukraine War

ABSTRACTWe apply a Time‐Varying Parameter Vector Auto Regressive (TVP‐VAR) connectedness approach on global assets to investigate time‐varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID‐19 and the Russia–Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID‐19 and the Russia–Ukraine war and that cumulative returns of portfolios are higher during COVID‐19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts.

Quantile time−frequency connectedness between energy and agriculture markets: a study during the COVID-19 crisis and the Russo−Ukrainian conflict

PurposeThis paper aims to examine the volatility connectedness between energy and agricultural commodities across different quantiles and time horizons.Design/methodology/approachThis study uses the quantile frequency connectedness approach on daily data spanning from January 2019 to November 2023.FindingsThe results indicate a sharp increase in total connectedness during the COVID-19 crisis and the Russian−Ukrainian conflict, suggesting that both the crisis and the war contribute to volatility spillover among energy and soft commodities. In fact, the findings suggest that, in the short term, the effects of the pandemic have a greater impact on dynamic risk spillover than those of the war. However, over the long term, the consequences of geopolitical tensions related to the war exert a more significant influence compared to the effects of the pandemic.Originality/valueThis study confirms that energy market prices and oil uncertainty play a significant role in explaining fluctuations in agricultural commodities across diverse timeframes, frequencies and quantiles. Particularly, at extreme quantiles, the results indicate that large shocks have a more pronounced impact than small shocks. These findings hold important implications for policymakers and market participants.

Exploring the unpredictable nature of climate policy uncertainty: An empirical analysis of its impact on commodity futures returns in the United States

AbstractThis study offers a nuanced examination of the interplay between climate policy uncertainty (CPU) and three categories of the US commodity futures returns (metals, energy, and soft commodities). By integrating a regression framework with a Markov regime‐switching approach, our results uncover both linear and nonlinear effects of CPU in varying volatility contexts. This comprehensive methodological approach sheds light on CPU's diverse impacts across various types of commodity futures. The findings illustrate CPU's notable influence on metal and energy futures in low‐volatility regime, contrasted with its more pronounced effect on soft commodities during high‐volatility regime. These insights are pivotal for investors strategizing in light of CPU, and underscore the significance of renewable energy in alleviating climate policy uncertainties within commodity markets.

Seeking a shock haven: Hedging extreme upward oil price changes

Crude oil is prone to large upward price shocks, creating challenges for personal and corporate budgeting and the economy more broadly. In this paper, we systematically assess a range of possible assets which might function as shock havens against large oil price shocks. Empirical tests uncover a rich set of assets which act both as hedges and shock havens, including precious metals, soft commodities and, with less consistency, currencies. The range of possible hedges is extensive, marked by a positive correlation with oil returns on average. Gold, silver, platinum, corn, and wheat are shown to provide consistent shock haven properties across various specifications, including those surrounding the large oil price changes observed during the COVID-19 Pandemic and following the invasion of Ukraine.

Exploring the dynamic links, implications for hedging and investment strategies between Sukuk and commodity market volatility: Evidence from country level analysis

This research paper examines the influence of spillovers between volatility of commodities (including soft commodities, precious metals, industrial metals, along with energy) and returns of sukuk. Using a notable sample of fifteen sukuk country indices and sixteen products, we examine the time-varying criterion vector autoregression (TVP-VAR) based extended joint connectedness method and contribute to the correlation analysis literature by supplying a comprehensive as well as policy-oriented analysis of the connection between sukuks and also commodities. Our results disclose that the system-wide dynamic connectedness is slowly heterogeneous and driven by financial occasions. Next, we look at the potential determinants of connectivity between sukuk and commodity markets, we find that global risk factors significantly impact the degree of spillovers between markets. In particular, the negative impacts of risk factors on spillovers suggest that some risk-mitigating properties may be related to market leverage in the composite portfolio in bear market conditions. In addition, our results, using hedging efficiency and the Sharpe ratio, confirm the hypothesis of diversification opportunities between markets that leverage dynamic connectivity networks.

Interconnectedness of Cryptocurrency Uncertainty Indices with Returns and Volatility in Financial Assets during COVID-19

This paper investigates the dynamic relationship between cryptocurrency uncertainty indices and the movements in returns and volatility across spectrum of financial assets, comprising cryptocurrencies, precious metals, green bonds, and soft commodities. It employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach; the analysis covers both the entire sample period spanning August 2015 to 31 December 2021 and the distinct phase of COVID-19 pandemic. The findings of the study reveal the interconnectedness of returns within these asset classes during the COVID-19 pandemic. In this context, cryptocurrency uncertainty indices emerge as influential transmitters of shocks to other financial asset categories and it significantly escalates throughout the crisis period. Additionally, the outcomes of the study imply that during times of heightened uncertainty, exemplified by events such as the COVID-19 pandemic, the feasibility of portfolio diversification for investors might be constrained. Consequently, the amplified linkages between financial assets through both forward and backward connections could potentially compromise financial stability. This research sheds light on the impact of cryptocurrency uncertainty on the broader financial market, particularly during periods of crisis. The findings have implications for investors and policymakers, emphasizing the need for a comprehensive understanding of the interconnectedness of financial assets and the potential risks associated with increased interdependence. By recognizing these dynamics, stakeholders can make informed decisions to enhance financial stability and manage portfolio risk effectively.

Capabilities of secondary school teachers in sub-Saharan Africa: A systematic literature review

Orientation: Education trends in Africa indicate that key ingredients for effective education are elusive, impacting the teachers who need to remain productive, motivated and healthy in this environment.Research purpose: Using machine learning active learning technology, the study aimed to review current literature related to the factors affecting the capabilities and functionings of secondary school teachers in sub-Saharan Africa (SSA).Motivation for the study: The Capability Approach (CA) provides a framework for studying the sustainable employability (SE) of teachers, including what they require to be able to convert valued opportunities into the needed achievements.Research approach/design and method: A systematic literature review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, using Machine Learning Active Learning Technology. Eighty six articles from 14 SSA countries were included for analysis, prioritising articles in the South African context first.Main findings: Analysis identified four groupings of resources that are potentially useful or valuable, creating access or empowerment if utilised effectively, namely knowledge commodities, soft commodities, hard commodities, and organisational commodities. Sub-resources were also identified.Practical/managerial implications: This research would assist policy and decision-makers to focus their interventions in the most effective way to sustain productivity and well-being in the workplace. The resource groupings should be included in a model that focuses on enhancing secondary school teachers’ capabilities to promote their well-being and productivity.Contribution/value-add: This article provides new applied knowledge related to machine learning active learning technology as a methodology, and provides further insight into secondary school teacher employability.

The Eligibility of Green Bonds as Safe Haven Assets: A Systematic Review

This study follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to examine the existing literature on the connectedness of green bonds with other markets as an attempt to highlight the effectiveness of green bonds in risk management and the benefits associated with incorporating green bonds in investment portfolios. An extensive search of relevant research papers to the scope of the review led to the identification of 31 articles published by February 2022. Our analysis traces the evolution of studies on green bonds’ interactions with other markets, the methodologies and data frequencies used for cross-market relations analysis, and the role of green bonds in portfolio risk management (diversifier, hedge, and safe-haven) in normal and extreme market conditions. The study reports several interesting findings. First, green bonds can be a strategic safe-haven avenue for investors in stocks, dirty energy stocks, and the foreign exchange market in the US and China in extreme market downturns. Second, green bonds demonstrated hedging properties against spillovers from Bitcoin, forex, soft commodities, and CO2 emission allowance. Third, the role of green bonds in the markets of natural gas, industrial metals, and crude oil is limited to a portfolio diversifier in different investment horizons. Fourth, green bonds had no diversification or hedge benefits for investors in conventional bonds. Fifth, the interrelationships between green bonds and most markets’ understudy were influenced by macroeconomic and global factors such as the COVID-19 pandemic, economic policy uncertainty, OVX, and VIX. Our review of the literature also facilitated identification of future research topics. The outcome of the review offers insightful information to investors in green bonds in risk management and assets allocation. Policy makers can benefit from this review in effective policy legislation for the advancement of the green bonds market and acceleration of a smooth transition to a net zero emission economy.

COVID‐19 and tail risk contagion across commodity futures markets

AbstractThis paper examines the impact of COVID‐19 on tail risk contagion across commodity futures markets using a copula‐based network method. We document a significant increase in the lower and upper tail contagiousness of commodities following the COVID‐19 outbreak. Contagion shows an obvious clustering characteristic, that is, there is higher tail risk connectedness between commodities in the same category. Agricultural commodities are significantly less contagious than metals and energy commodities; soft commodities in particular can offer investors significant diversification benefits. There are several hub commodities in the contagion network, chief among them copper, which are good transmitters of shocks and should be treated with caution by investors and regulators. Although tail risk and contagiousness of individual commodities increase together during the pandemic, we find a negative cross‐sectional relationship between tail risk and contagiousness, that is, commodities with high tail risk are not necessarily highly contagious and may even be less so.

A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles

PurposeThis work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.Design/methodology/approachData evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).FindingsThe empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.Originality/valueThe authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

Time-frequency connectedness between energy and nonenergy commodity markets during COVID-19: Evidence from China

We aim to investigate the static and dynamic time-frequency connectedness between energy and nonenergy commodity markets in China during COVID-19 based on Baruník and Křehlík (2018) method. First, in this paper, we find that the short-term connectedness dominates the long-term one, and the total connectedness increases after the COVID-19 outbreak. Second, the energy commodity is the receiver and is influenced much by the spillovers of non-energy commodity markets (e.g. chemical commodities and non-ferrous metals) in the short run. At the same time, the impact is less at the long-term investment horizons. In addition, chemical commodities and soft commodities are the primary transmitters in this system in the short run. In contrast, chemical commodities and coal steel iron commodities are the main long-run primary transmitters. Third, the spillover role varies with the time-frequency domain during COVID-19. To be more specific, the energy commodity shows a net receiver role in the short and long run before the COVID-19 pandemic, but after it, the role of the net transmitter can be seen in the long run with ease. Finally, we show that COVID can reduce the hedging effectiveness at different investment horizons. The mineral policymakers should note our dynamic empirical results between energy and nonenergy commodity.

Measuring the risk-adjusted performance of selected soft agricultural commodities

In this paper, we used several elaborate return-to-risk methods to investigate the risk-adjusted performances of five soft commodities. Regarding only the level of risk, we found that cocoa had the highest risk of losses, followed by orange juice. Cotton and coffee had the lowest risk of losses. However, according to the return-to-risk output, cotton was the worst asset in which to invest because it had negative average returns. In contradistinction, sugar had a relatively high risk of losses but also the highest average returns, which put it in the first place according to the Sharpe, Sortino and modified Sharpe ratios. Although orange juice had the second-worst downside risk performance, it came in second place according to the return-to-risk ratio because it had relatively high average returns.

The prices of renewable commodities: a robust stationarity analysis*

This paper addresses the problem of testing for persistence in the effects of the shocks affecting the prices of renewable commodities, which have potential implications on stabilisation policies and economic forecasting, among other areas. A robust methodology is employed that enables the determination of the potential presence and number of instant/gradual structural changes in the series, stationarity testing conditional on the number of changes detected and the detection of change points. This procedure is applied to the annual real prices of eighteen renewable commodities over the period of 1900–2018. Results indicate that most of the series display non‐linear features, including quadratic patterns and regime transitions that often coincide with well‐known political and economic episodes. The conclusions of stationarity testing suggest that roughly half of the series are integrated. Stationarity fails to be rejected for grains, whereas most livestock and textile commodities do reject stationarity. Evidence is mixed in all soft commodities and tropical crops, where stationarity can be rejected in approximately half of the cases. The implication would be that for these commodities, stabilisation schemes would not be recommended.

Are soft commodities markets affected by the Halloween effect?

Within the last three decades commodity markets, including soft commodities markets, have become more and more like financial markets. As a result, prices of commodities may exhibit similar patterns or anomalies as those observed in the behaviour of different financial assets. Their existence may cast doubts on the competitiveness and efficiency of commodity markets. It motivates us to conduct the research presented in this paper, aimed at examining the Halloween effect in the markets of basic soft commodities (cocoa, coffee, cotton, frozen concentrated orange juice, rubber and sugar) from 1999 to 2020. This long-time span ensures the credibility of results. Apart from performing the two-sample t-test and the rank-sum Wilcoxon test, we additionally investigate the autoregressive conditional heteroskedasticity (ARCH) effect. Its presence in our data allows us to estimate generalised autoregressive conditional heteroskedasticity [GARCH (1, 1)] models with dummies representing the Halloween effect. We also investigate the impact of the January effect on the Halloween effect. Results reveal the significant Halloween effect for cotton (driven by the January effect) and the significant reverse Halloween effect for sugar. It brings implications useful to the main actors in the market. They may apply trading strategies generating satisfactory profits or providing hedging against unfavourable changes in soft commodities prices.

Safe-haven properties of soft commodities during times of Covid-19

We use wavelet coherence analysis on global COVID-19 fear index and, soft commodities’ spot and futures prices to investigate safe-haven properties of soft commodities over the period from January 28, 2020 to April 29, 2021. Our findings show that each of the sampled soft commodities shows safe-haven behavior in one of the spot or futures markets and for one of the short-term or long-term investors during the times of COVID-19. Our results also show that safe-haven properties of soft commodities are contingent upon the nature of the commodity. The findings of our mean-variance portfolio analysis indicate that the portfolios with commodity futures are less risky and efficient compared to the portfolio containing stocks only, thus robustly supporting the safe-haven properties of soft commodities during COVID-19. Our results not only have important implications for individual investors and asset managers in suggesting particular soft commodities to strengthen safe-haven and diversification features of their portfolios but also can assist the policy makers to understand and disentangle health fear dimension of several interlocking dynamics affecting the spot and futures prices of soft commodities during COVID-19.

The soft commodities multiple bubbles tests: evidence from the New York Futures Markets

ABSTRACT The soft commodity is a high-frequency trading market. We use the right-tailed unit root tests of Phillips et al. to investigate asset bubbles within and to detect explosive episodes on each soft commodity sample date set. Our empirical test uses weekly data from New York soft commodity market and finds bubbles existed in each of the soft commodities. The test model allows us to exam each bubble’s origination and termination dates using date-stamping in the recursive procedure. We found that the soft commodities bubble is more relevant to the man-made factor.

Commodity terms of trade shocks and real effective exchange rate dynamics in Africa's commodity-exporting countries

Motivated by the absence of conclusive guidance from the current literature, this study revisits the effects of terms of trade shocks on real effective exchange rates (RER) across twenty-three primary commodity-exporting countries in Africa. To address the heterogeneity across primary commodities that may have plagued earlier studies, we employed a nonlinear panel ARDL approach to capture both the cross-section and time variations across primary commodities. Our analysis offers three main finding patterns. Firstly, we highlighted that the response of the RER to terms of trade shocks is asymmetric: the real appreciation is more pronounced for positive than negative shocks in terms of trade in the long-run while negative shocks in terms of trade cause the RER to depreciate in the short-run. Secondly, we found that the asymmetric responses of RER differ across commodity subgroups and seem to matter more for energy-exporting countries. Finally, we showed that energy and metal commodity-exporting subgroups are the most subject to real appreciation in the long run in comparison to countries exporting soft commodities such as agricultural and, food and beverage commodities. A fundamental policy corollary follows that there is a need to remediate the loss of the external competitiveness associated with real appreciation by coordinating monetary and fiscal policies to effectively absorb the huge additional foreign reserves and ensure an exchange rate equilibrium level, which will bring macroeconomic stability in primary commodity-exporting countries.

Hard Landing of Soft Commodities in 2019 (The Domino Effect or The Paradox of Plenty???) (Presentation Slides)

Paper Hard Landing of Soft Commodities in 2019 : demystifies prevailing condition in soft commodity market and what will be way forward in 2019. (The Domino Effect or The Paradox of Plenty???)

Future of Soft Commodities – During the Time of War (Trade War) (Presentation Slides)

Future of Soft Commodities - During the time of (TRADE War)” the paper title mentioned cannot have simple and definitive answer. Reason being the world is too varied , too complex, for that to be possible. However, its not hard to recognise the sharp differences in ability to identify the more prominent and influential actors. One must understand purpose of the war is to degrade the enemy's will and economic capacity. It may sound surprising but destruction through a market attack can be more effective than taking out enemy's ship, aircraft, deploying chemical weapons, etc when it comes to disabling an opponent. During 2015-16 “Currency/Financial” war was evident as boundaryless warfare with China devaluating its currency. Post which there has been major change in global market dynamics, in fact has only got more intensified with change in leadership across world. The new course opted is TRADE WAR wherein most countries has started to deploy it's only TRUMP CARD of TARIFFS and leading the pack / more prominent actor in forefront is US of A. Such war can clamp down countries or even global banking system: Banking stops, Credit stops, and when Credit stops, Trade stops and when Trade stops, ECONOMY CRUMBLES. Economist John Maynard Keynes stated When the facts change, I change my mind“ Adhering with Mr. Keynes statement looks like we need not change our battlefield as is ON just that now it is more open & intense with many players joining the bandwagon Hence, Are Commodities getting politicised than staying course with its fundamentals???“ What challenges & opportunities does it get along???

CAN INDIVIDUAL INVESTORS BENEFIT FROM TECHNICAL ANALYSIS IN MARKETS OF SOFT COMMODITIES? EMPIRICAL STUDY FOR 2010–2018

After the 2008 financial crisis, many investors diversified their portfolios with different commodities, including the so-called softs. This paper aims to answer the question of whether individual investors can benefit from technical analysis on soft commodity markets. The empirical study is based on daily quotations of six soft commodities: coffee, cocoa, sugar, cotton, rubber and frozen concentrated orange juice from 2010 to 2018, and investigates the profitability of applying indicators and oscillators based on moving averages with different length. The results show that the application of five-day simple and weighted moving averages and momentum oscillators was most effective, providing positive returns in five out of six soft commodities markets.