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We investigate the volatility dynamics of commodity price and the dependence structure between commodity prices and output growth in the G7 and EM7 economies using a semiparametric GARCH-in-Mean copula approach. We show that for the G7 economies, a symmetric weak tail dependence exists between commodity prices and outputs in France, Germany, and Japan. For the EM7 economies, a lower tail dependence is observed between commodity prices and output growth in Brazil, and a symmetric weak tail dependence is observed in Indonesia. No statistically significant tail dependence between commodity prices and output growth is found for the rest of the G7 and EM7 economies.

This study aims to investigate the relationship between the spot and futures commodity markets. Considering the complexity of the relationship, we use a nonlinear autoregressive distributed lag (NARDL) framework that considers the asymmetry and nonlinearity in both the long and short run. Based on the daily returns of six commodity indices reaggregated on three commodity types, our study reaches some interesting findings. Our analysis highlights a bidirectional relationship between both markets over the short and long run, with a greater lead for the futures market. This result confirms the future market’s dominant contribution to price discovery in commodities. Changes in commodity prices appear first in the futures market, as informed investors and speculators prefer trading on this market that is characterized by low costs and a high-leverage effect. Then, the information is transmitted from the futures to the spot market through arbitrageurs’ activity, which explains the nonlinearity of the relationship. These results are helpful to scholars, investors and policymakers.

This article aims to uncover the effects of the COVID-19 pandemic on the energy markets in terms of energy stock indexes, energy futures, ETFs, and implied volatility indexes. We model the volatility of energy markets and demonstrate the effects of various phases of the pandemic outbreak (COVID-19) on the energy market. COVID-19-induced uncertainty indicators like the growth of the infection, economic policy uncertainty (EPU), and infectious diseases market volatility (IDsMV) have shown pronounced effects on energy markets’ historical volatility. The volatility of energy ETFs–stocks appears to be more resilient in line with S&P 500 energy stocks. WTI crude oil market has shown an unprecedented overreaction amid pandemic outbreaks and traded with an extreme volatility level. The investors’ sentiment in the energy market was factually higher on the tail events, indicating that fearful investors rushed toward put options and paid an excess premium to protect from unparalleled risk in the energy market.

This article contributes to the existing empirical literature by examining the spillovers across price inflation and agricultural commodity prices for the case of Nigeria. To achieve this objective, we employ the Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) spillover index. Subsequently, we examine the directional spillover, total spillover, and net spillover indexes. Further analysis to capture cyclical and secular movements was addressed with 40 months of subsamples via the rolling window analysis. Our empirical results, based on the monthly frequency data from January 2006 to July 2016 show that the total spillover effect was about 75%. This suggests a high interconnectedness of the selected agricultural commodity prices and inflation. Further empirical findings shows that inflation, sorghum, soybeans, and wheat were net receivers while cocoa, barley, groundnut, maize, rice were net givers. We find a negative net spillover for price inflation, implying a net positive spillover from commodity prices to price inflation. Based on these outcomes, several inherent policy implications for the government administrators, farmers, investors and all stakeholders abound. For instance, the need for government officials to insulate the agricultural market from externalities for optimum prices stability is pertinent.

This study explains the effects of crude oil prices on copper and maize prices. Vector autoregressive and vector error correction models are used to study the relationship between oil prices and prices of copper and maize. The commodity price data used consist of average monthly prices of each of the commodities: crude oil, copper and maize for the months January 1982 to June 2021. For robustness, the analysis was also run on a sample of the same data for the period January 2000 to June 2021. A long-run relationship was found between crude oil and copper prices on the one hand and maize prices on the other for the 1982 to 2021 period at the 5% significance level. The same was not true for the shorter sample (2000 to 2021). Granger causality flowing from crude oil prices alone to copper and maize prices was not found. Recommendations that are useful for energy, mining, agriculture and general development policy and practice are made. The findings are also useful for bilateral and multilateral aid discussions. The limitations of the study and recommendations for future scholarship are also made.

This paper provides a general equilibrium theoretical model of alcohol taxation and empirically estimates this model. For this purpose, we use a model determined by both externality corrections and fiscal considerations as the tax increase is assumed to immediately change other governmental policies such as labour taxation or medical expenditures. The results of our analysis show that under the most of parametric scenarios the current Czech tax rate on beer and wine is below its optimal level and that the fiscal component has a significant impact on the optimal level of tax.

This paper investigates how oil price changes affect consumer price inflation in eleven Central and Eastern European countries. We use a wavelet-based Markov switching approach in order to distinguish between the effects at different time horizons. We find that the transmission of oil price changes to inflation is relatively low in the Central and Eastern European countries as an increase in the oil price of 100% is followed by a rise in inflation of 1–6 percentage points. The strongest impact from rising oil price on inflation is found for the longer time-horizons for most of the countries, which means that the indirect spillover effect is more intensive than the direct one. Also, the results indicate that exchange rate is not a significant factor when oil shocks are transmitted towards inflation, except in the occasions when high depreciation occurs. Slovakia and Bulgaria are the countries which experience the highest and most consistent pass-through effect throughout the observed sample, and this may be due to these countries having some of the highest oil import/GDP ratios.

The research investigated the impact of macroeconomic variables on the volatility of the commodity futures market in India (together with oil futures, agricultural commodity futures and metal futures). The monetary policies, financial market information and economic environments are determined by the macroeconomic variables. The low-frequency macroeconomic variables and daily price volatility is studied in the research employed by the GARCH-MIDAS model. This model simplifies the series of volatility into long- and short-run modules, which allow for the testing of the macroeconomic variables can control the long-run variance or not. The current study reveals the effect on long-run volatility factor in the commodity market, and the majority of verified data have shown that low-frequency variables have a positive impact in the long-run variance of the commodity futures market. The outcome of the study suggested that the national and international economic variables perform a substantial part in assessing the price volatility of the commodity futures market in India.

We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime-dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict adverse movements and returns implied by a trading strategy. Our analysis provides overwhelming evidence that allowing for regime-dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub-indices (energy, industrial metals, precious metals, agriculture, livestock). Our results suggest the existence of a trade-off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.

Existing literature has not yet identified the common determinants of price volatility transmission in agricultural commodities from international to local markets and has rarely investigated the role of self-sufficiency measures in the context of national food security. We analyzed several factors to determine the degree of volatility transmission in wheat, rice and maize prices between world and domestic markets using GARCH models with dynamic conditional correlation specifications and panel feasible generalized least square models. Our findings indicate that a grain autarky system can reduce volatility passthroughs for three grain commodities. While the substitutive commodity consumption behaviour between maize and wheat buffers the volatility transmissions of both, rice does not function as a transmission-relieving element for the volatility implying that rice is not a substitute for wheat or maize consumption; grain consumption proves a more effective substitute than cereal self-sufficiency for insulating passthroughs from global markets. These findings may help the governments of developing nations to protect their domestic food markets from the uncertain movements of foreign markets and may thus improve food security.

Food price inflation results in uncertainty in the food markets and reduces real income asfood covers a relatively large share of the households’ expenditures in the LDCs. As priceof food commodities are primarily governed by the underlying demand and supplyconditions, we have analyzed the association of futures price volatility with the underlyingmacroeconomic variables. A strong association of futures price volatility with theunderlying macro variables will imply that futures market operates based on theimplications of the macroeconomic policies and are not merely driven by speculativemotive. The association between futures price and the macroeconomic variables will helpin developing policies aimed at stabilizing food prices. For our study we have consideredthe five major oil and oilseed contracts traded on National Commodity and DerivativesExchange. We have considered the nearest three month contracts traded on the exchange.In our study we observe that Gross Domestic Product (GDP) and Index of IndustrialProduction (IIP) growth rate have significant impact on futures price volatility. We havealso found a significant relation between futures price volatility and inflation. Thesefindings have important implications for commodity production decision making,commodity hedging and commodity price forecasting.&nbsp

El Niño Southern Oscillation (ENSO) is a naturally occurring phenomenon that affects weather around the world. Past ENSO episodes have had severe impacts on the economy of Colombia. We study the influence of ENSO on Colombian coffee production, exports, and price. Our structural econometric specification is consistent with an economic model of the market for Colombian coffee which, in the short run, is characterized by a downward-sloping demand curve and by a vertical supply curve. We show that El Niño (i.e., positive shocks to ENSO) is beneficial for Colombian production and exports and decreases the real price of Colombian coffee. On the contrary, La Niña (i.e., negative shocks to ENSO) depresses Colombian coffee production and exports and increases price. However, the overall impact of ENSO shocks is small. Both in the short run and in the long run, shocks to international demand for Colombian coffee are more relevant than supply-side shocks in Colombia in explaining the dynamics of the price of Colombian coffee. Our results suggest that a given coffee price shock can have beneficial, detrimental, or negligible effects on the Colombian economy, depending on its underlying cause. As a consequence, policy responses to coffee price shocks should be designed by looking at the causes of the shocks.

Many studies point out the growing correlations within financial markets, while others highlight the financialization of commodity markets. The purpose of this article is to revisit the relationships between various financial assets and commodity markets by taking into account the U.S. monetary policy and therefore the implementation of non-standard measures. In addition to oil, stock and bond markets, U.S. policy rates and a great deal of agricultural prices have been over time considered through a DCC-GARCH model, between 1995-2015. We find that agricultural markets uphold the financialization hypothesis, implying an increase in market-prices’ correlations and so raises the question of agricultural prices’ drivers. Interestingly, conditional correlations between the U.S. monetary policy and agricultural prices have decreased since 2010, which indicates that the implementation of non-standard monetary policy measures reduces spillover effects on asset prices, especially raw commodities. Such a result in turn highlights changing relationships between monetary, financial and physical markets, in a context of very weak policy rates over a long period.

It is argued that business cycles have been moderating. However, there are a limited number of studies in the literature analyzing the cyclical behaviour of commodity prices in the last decades. This paper attempts to fill this gap by investigating the super-cycles in oil prices. In addition, interdependence between cycles in GDP and oil prices and co-movements between cycles in oil and other commodity prices are investigated. Results show that super-cycles in oil prices still exist and are not moderating. The last peak in long-term oil price was observed in 2012 and since then it has been on a downward trend.

Financial times series, and commodity prices in particular, are known to exhibit fat tails in the distribution of prices. As with many natural resources price series, the arrival of new information can lead to unexpectedly rapid changes&mdash, or jump&mdash, in prices. This suggests that natural resource commodity prices should follow a more complex process than geometric Brownian motion (GBM), which is linked to the Gaussian distribution. The presence of jumps (discontinuities) in several heavy metal price series is investigated, as well as time-varying volatility. The results demonstrate that allowing for jumps and time-varying volatility provides statistically important improvements in the modelling or prices, relative to GBM. These complex processes contributed to the fatness of the tails in the distribution of heavy metal price returns.

Highlights • - Cryptos with small market capitalization are more likely to be sources of shocks than their larger counterparts. • - Bitcoins can be considered as a better hedge due to its relative independence. • - USDT strong anchoring with US$ makes it very volatile. • - The idiosyncrasy of Gold enables it to weather adverse crypto market’s movements. • - Having gold in the portfolio with cryptocurrency helps fruitful diversification., The cryptocurrencies with small market capitalization are often overlooked despite they can potentially be the source of shocks to other cryptocurrencies in the market. To address this caveat, this paper attempts to investigate the spillover effects among 14 cryptocurrencies by employing transfer entropy. Our results suggest that among different types of cryptos, Bitcoin is still the most appropriate instrument for hedging, while Tether (USDT) which have a strong anchor with the US dollar is significantly volatile. Interestingly, we document that the small coins are more likely to be shock creators in the cryptocurrency market. Using the same approach, we further explored the link between gold prices and cryptocurrency prices. The results show that gold could be a good hedging instrument for cryptocurrencies due to its independence. In light of empirical results, it is advisable to carefully consider the coins with small capitalization. Further, investors should conduct portfolio rebalancing by including gold to hedge against the unexpected movement in the cryptocurrency market. Our paper not only contributes in terms of the application of advanced empirical methodology but also provides evidence on idiosyncratic features of the cryptocurrency market.

This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors&rsquo, positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility.

We consider the problem of optimal trading for a power producer in the context of intraday electricity markets. The aim is to minimize the imbalance cost induced by the random residual demand in electricity, i.e. the consumption from the clients minus the production from renewable energy. For a simple linear price impact model and a quadratic criterion, we explicitly obtain approximate optimal strategies in the intraday market and thermal power generation, and exhibit some remarkable properties of the trading rate. Furthermore, we study the case when there are jumps on the demand forecast and on the intraday price, typically due to error in the prediction of wind power generation. Finally, we solve the problem when taking into account delay constraints in thermal power production., 39 pages, 11 figures

Purpose The purpose of this paper is to examine, in a global perspective, the oil, gold, US dollar and stock prices interdependencies and to identify instantaneously direct and indirect linkages among them. Design/methodology/approach A methodology based on simultaneous equations system was used to identify direct and indirect linkages for the period 1995-2015. The authors try initially to find theoretical answers to main question of the study by discussing causal bilateral relationships while focusing on multilateral interactions. Findings The results show significant interactions between all markets. The authors found a negative relation between oil and stock prices but oil price is significantly and positively affected by gold and USD. Oil price is also affected by oil futures prices and by Chinese oil gross imports. Gold rate is concerned by changes in oil, USD and stock markets. The US dollar is negatively affected by stock market and significantly by oil and gold price. Indirect effects always exist which confirm the presence of global interdependencies and involve the financialization process of commodity markets. Originality/value Motivation of this research paper is the substantial implications of price movements on real economy and financial markets. Understanding that co-movement has great value for investors, policy makers and portfolio managers. This paper differs from previous studies in several aspects. First, most of the research papers focus on bilateral linkages solely, while the authors’ investigation was implemented on all the four markets simultaneously. Second, the study was developed in a global framework using international data. The global analysis allows avoiding country specific effects.

In this study, I apply a quantile regression model to investigate how gold returns respond to changes in various financial indicators. The model quantifies the asymmetric response of gold return in the tails of the distribution based on weekly data over the past 30 years. I conducted a statistical test that allows for multiple structural changes and find that the relationship between gold return and some key financial indicators changed three times throughout the sample period. According to my empirical analysis of the whole sample period, I find that: (1) the gold return rises significantly if stock returns fall sharply, (2) it rises as the stock market volatility increases, (3) it also rises when general financial market conditions tighten, (4) gold and crude oil prices generally move toward the same direction, and (5) gold and the US dollar have an almost constant negative correlation. Looking at each sample period, (1) and (2) are remarkable in the period covering the global financial crisis (GFC), suggesting that investors divested from stocks as a risky asset. On the other hand, (3) is a phenomenon observed during the sample period after the GFC, suggesting that it reflects investors&rsquo, behavior of flight to quality.

Food commodity prices escalated during the 2007/2008 food crisis, and have scarcely fallen since. We show that high fertilizer prices, driven by the formation of an international export cartel as well as high energy prices, explains the majority of the recent price spikes. In particular, we estimate the pure fertilizer cartel effect explains more than 60% of crisis food price increases. While population growth, biofuels, high energy prices and financial speculation doubtlessly put stress on food markets, our results help to understand the severity and sudden emergence of the crisis and suggest avenues to prevent its repetition.

In this paper we use the frequency domain Granger causality test of Breitung/Candelon (2006) to analyse short and long-run causality between energy prices and prices of food commodities. We find that the oil price Granger causes all the considered food prices. However, when controlling for business cycle fluctuations this link exists especially at low frequencies. Thus, short-run phenomena like herd behaviour and speculation do not seem to have a considerable effect on the studied food prices. The relation between oil and food prices is rather established by long-term developments. A possible explanation for this could be the production of biofuel.

We study the impact of global food price shocks on domestic inflation in a large group of countries. For advanced economies, a 10% increase in global food inflation raises domestic inflation by about 0.5 percentage point after a year; however, the impact has declined over time and become less persistent. The global food price shocks of the 2000s had a much bigger impact on domestic inflation in emerging and developing economies than in advanced economies. This could reflect the smaller share of food in the consumption baskets in advanced economies. We also provide evidence that inflation expectations are more anchored in advanced than in emerging economies, which could also explain the smaller impact on inflation from global food price shocks.