Commodity Price Index Research Articles

Does the S-curve demonstrate an asymmetrical response to fluctuations in exchange rates?

Purpose This study aims to examine the asymmetric S-curve between the trade balances of Pakistan and China at the commodity level using disaggregated data. Design/methodology/approach This study focuses on Pakistan and China bilateral trade based on commodity-level data. This study delves into the S-curve phenomena by examining time series data from 1980 to 2023 across 32 three-digit industries/commodities. Findings The findings show significant evidence in favor of the “asymmetric S-curve” in 27 out of the 32 industries studied. This study confirms that the devaluation of home currency is not a viable solution always to improve trade balance. Research limitations/implications This study considers 32 three-digit industries limiting the generalizability of findings. Due to data unavailability, the authors fail to consider other industries. In the absence of quarterly data on industry-level trade between Pakistan and China, annual data from 1980 to 2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to the general consumer price index with its inherent aggregation issues, whereas this study has opted for commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity level. Practical implications The findings have practical relevance in guiding policy decisions regarding commodity trade, whereas the industry-wise analysis enriches the understanding of the short-term effects of currency depreciation on trade balance dynamics. Originality/value The S-curve hypothesis predicts a negative cross-correlation between a country's current exchange rate and its past trade balance and a positive cross-correlation between the current exchange rate and its future trade balance. Previous empirical S-curve studies had the limitation of assuming symmetry in cross-correlation with both current and future trade balance values.

Portfolio balance effect of the U.S. QE between commodities and financial assets in commodity-exporting countries

This study analyses the portfolio balance channel of the U.S. quantitative easing (QE) by assessing the dynamic spillover effect between commodities and financial assets in commodity-exporting countries during QE. This study integrates the generalized spillover index initially proposed by Diebold and Yilmaz (2012) for the fractional integration VAR model. Then, we estimate the multivariate framework of the Westerlund and Narayan (2015) (WN)-based predictive model to quantify the effect of the portfolio balance channel on the net pairwise spillover index from the U.S. to other countries. Our results show: first, for bond yields, that Asian and Pacific bond yields are impacted by both commodity price indices returns and the U.S. bond yields across the sample periods. However, mixed evidence is found for both Latin America and Others; second, for equity, dynamic net return spillovers contribute mixed evidence across regional groups during QE. The diverse results are partly explained by the average percentage of commodity exports to total exports of the country and the degree of close interrelationship between countries. Additionally, dynamic return spillover analyses show that most foreign exchange returns are negative net spillovers during QE, supporting the behavior of “commodity currencies.” Last, the WN-based predictability models show pronounced differences in predictability across the selected commodity-exporting countries.

Global Supply Chain Disruptions, Commodity Price Shocks and Inflation in Japan

In recent years, the concurrent occurrences of the COVID-19 pandemic and the Russian invasion of Ukraine have led to global disruptions in supply chains and a surge in commodity prices. Major advanced economies have experienced an increase in inflation rates and a decline in economic activity. Against this economic backdrop, this paper aims to address the strained condition of supply chains by employing the Global Supply Chain Pressure Index (GSCPI) alongside commodity price indices. Furthermore, it seeks to examine how fluctuations in these variables affect Japan’s macroeconomic landscape through local projection and structural Vector Autoregression (VAR). In the local projection analysis, it has been observed that the strain on supply chains induces sustained increases in consumer and producer prices, further contributing to long-term declines in production. Within the structural VAR model framework, the identification of structural shocks is conducted through sign restrictions, affirming the significance of supply chain shocks and energy and food price shocks as crucial factors contributing to recent price increases. This confirmation is supported by variance decomposition and historical decomposition analyses.

Measuring persistent global economic factors with output, commodity price, and commodity currency data

AbstractIn this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non‐persistent factor, both as a single global factor using all data and as factors for each category of data. The in‐sample predictive performances of the three persistent factors together are better than the non‐persistent factors and the single global factors. Out‐of‐sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub‐categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1‐ to 6‐month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12‐month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate‐based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.

Trading commodity ETFs: Price behavior, investment insights, and performance analysis

AbstractThis study analyzes the risk‐adjusted performance of commodity exchange‐traded funds (ETFs) across diverse market conditions. Examining monthly returns from December 2004 to June 2022, our findings suggest that commodity ETFs underperformed US stocks, indicating limited diversification benefits. However, we document positive α during turbulent market periods like the COVID‐19 crisis, signifying potential resilience. Furthermore, our factor regressions reveal that shifts in the global commodity price index and disposable personal income significantly influence commodity ETFs' excess returns, pointing to broader economic and income‐related trends. Commodity ETFs exhibit lower Value‐at‐Risk and Expected Shortfall compared to stock market indices, indicating reduced downside risk exposure for investors. Given the increasing popularity of commodity ETFs, these insights hold substantial significance for market participants.

HWWI Commodity Price Index: A Technical Documentation of the 2023 Revision

Abstract The HWWI Commodity Price Index, which aims at reflecting price developments of raw materials imported by highly industrialized countries, dates back to the 1950s. This paper explains the calculation of the index at the example of its latest revision, which became effective on 1st September 2023. While the index and its subindices are still calculated for OECD and European Momentary Union countries at four different frequencies (annual, monthly, quarterly and daily) for two different currencies (USD, EUR), it now additionally includes index values for several weighting periods (long-term: 2012–2021, pre-crisis: 2017–2019 and crisis years: 2020–2021), for individual countries and for different perspectives (daily revised, real-time and historical time series). The paper also provides detailed information on the employed methodology and the input data (commodities, import data, prices) used to calculate the weights and the index values. Although the revision is not without effect on the resulting index values, especially throughout the crises periods, the correlation between the initial and the revised index remains very high.

Monetary Policy Measures and Commodity Price in Nigeria: An Auto-Regressive Distributive Lag Approach

This study investigated the impact of monetary policy measures on commodity price in Nigeria from 1990 to 2022. Data for the study were obtained from the Central Bank of Nigeria data source and the World Bank’s World Development Indicators 2022. Real interest rate, cash reserve ratio, Broad money supply, Treasury bill rate, and exchange rate are adopted to proxy monetary policy while commodity price index is employed to capture commodity price. The individual series were subjected to unit root tests using the Augmented Dickey Fuller approach and the diagnoses established mixed orders of I(0) and I(1) integrations, thereby necessitating application of the Auto-Regressive Distributive lag bounds test as well as the long-run and error correction versions. The analyzed model’s result established that real interest rate is positive but statistically insignificant with commodity price in the long-run and the most current year period of the short-run while cash reserve ratio had a positive and significant relationship with regress and in the long-run. Also, it was evident that broad money supply had a negative but significant relationship with commodity price in the current period of the short-run. Moreso, Treasury bill rate and commodity price revealed that the former had a negative but significant relationship with the latter in the current period of the short-run. Finally, the official exchange rate exerted a positive but insignificant ramification on commodity price both in the long-run and previous year period of the short-run. Hence, it is concluded that monetary policy fundamentals as captured here had significant push-up effect on commodity price in Nigeria. Thus, the need for suggesting amongst others that the monetary policy authority should attractively peg the price of federal government’s securities, moderate money supply and interest rate to enhance monetary stability for driving diversified private sector investments which if sustained will dampen commodity price in the long-run. Keywords: Monetary Policy, Commodity Price, Real Interest Rate, Cash Reserve Ratio, Treasury Bill, Exchange Rate Broad Money Supply.

Commodity prices and domestic credit in Central and Eastern Europe: Are there asymmetric effects?

Fluctuations in commodity prices can have an impact on a firm’s costs and revenues, national income, or a country’s creditworthiness, leading to increased borrowing and levels of domestic credit. These effects need not be symmetric; it is possible that losses due to a commodity price decrease might be worse than the gains that result from an equivalent price increase. In addition, these dynamics might differ between frequently studied commodity exporters and energy importers such as those in Central and Eastern Europe. Here, an index of commodity prices and their volatility are included in a time-series model alongside the traditional macroeconomic determinants for 11 of these EU members. Forecast Error Variance Decompositions reveal that shocks to commodity prices spill over most strongly to inflation in Latvia and credit growth in Poland and Slovakia—the two countries that have seen continuous increases in credit. Cointegration analysis shows that while GDP growth and inflation drive credit levels in many cases, commodity price increases lead to increased credit shares in the Czech Republic, Latvia and Lithuania. A nonlinear model finds that commodity price increases also increase credit in Romania, while decreases lead to increased credit shares in Hungary. Commodity price volatility leads to credit increases in Latvia and Lithuania, confirming that these two Baltic countries are most affected by macroeconomic shocks.

Exponential Smoothing State Space Innovation Model for Forecasting Export Commodity Price Index in Nigeria

Exponential Smoothing State Space Innovation Model for Forecasting Export Commodity Price Index in Nigeria

Investigation into the dynamic relationships between global economic uncertainty and price volatilities of commodities, raw materials, and energy

Purpose This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020. Design/methodology/approach The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while. Findings The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions. Originality/value This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

Predicting gold volatility: Exploring the impact of extreme risk in the international commodity market

This paper mainly examines the performance of the tail risk (TR) constructed by 19 sub-categories of commodity price indices for predicting gold futures market volatility. Under the framework of the AR model, we compared the predictive capabilities of AR-type models incorporating the TR indicator as an additional explanatory variable against the AR benchmark model without the TR indicator. Our findings reveal that the incorporation of the TR indicator significantly improves the predictive accuracy of the gold futures volatility model. Notably, this enhancement is particularly pronounced during periods of heightened volatility.

Inflation surge of 2021-2023 and monetary policy in Eastern European countries

This article provides an in-depth analysis of the current global economic landscape, with a particular focus on Moldova, Ukraine, Romania, and Poland. The scope of this article is to examine central banks’ responses to the challenges posed by 2021-2023 inflation. The research employs a combination of scientific methods, including empirical analysis and quantitative research. It utilizes data from reputable sources like the World Bank, National Banks, and National Bureaus of Statistics to examine various economic indicators. Statistical software R is used for data analysis, including Cross-Correlation Function (CCF) analysis. In response to rising inflation, Moldova and Ukraine raised interest rates significantly. Moldova later eased rates gradually, while Ukraine maintained a high rate to safeguard its currency. Romania and Poland, with stronger economies, saw milder rate hikes and less inflation increase compared to other Eastern European nations, adopting a cautious and delayed approach and still not reducing their base rates. A critical aspect of the research is the implementation of cross-correlation function (CCF) analysis, which is used to explore the time-lagged correlations between different economic variables, such as the Base policy rate, Commodity price index and IPC. The analyses conducted have revealed two significant patterns: a deficiency in the proactive adjustment of base rates by central banks in response to inflation and the lead-lag relationship between ascending commodity prices and inflation. These findings underscore the cautious approach taken by central banks and their delayed response to the impact of external factors on inflation dynamics.

Extreme risk spillover effect and dynamic linkages between uncertainty and commodity markets: A comparison between China and America

This study aims to assess the extreme risk spillover effect and dynamic linkages between uncertainty and commodity markets. We consider geopolitical risk, economic policy uncertainty, and climate policy uncertainty to measure uncertainty shocks and compare two common commodity markets spanning China and America. Then, the quantile-based TVP-VAR method is used to conduct an empirical study using monthly data from May 2008 to August 2021. Our findings demonstrate that, first, the interaction between uncertainty and commodity markets is particularly vulnerable to extreme shocks, with greater lower quantile spillover effects than those at the upper quantile. Second, there is a time-varying variation in the spillover effects between uncertainty and commodity markets, and the spillovers are vulnerable to shocks from a significant crisis. Third, the impulse response of commodity price indexes to the uncertainty indexes is time-varying, with shocks fluctuating sharply in the short run and stabilizing over the long term. Finally, compared to American commodity markets, Chinese commodity markets exhibit greater complexity and higher volatility in their initial responses to uncertainty shocks. In most cases, the US commodity market is a transmitter of risks, while the Chinese commodity market often receives risks, with uncertainty shocks acting as both the transmitter and receiver. These intriguing findings can serve as a guide to prevent market risks and optimize investment decisions.

Exploring The Global Slack Hypothesis On Inflation Dynamics: A Study In ASEAN-5

Inflation is one of the economic problems that often occur in ASEAN-5 countries. External factors affecting inflation such as changes in energy prices and commodity prices (agriculture, metals, and minerals) can affect inflation in ASEAN-5 countries through the influence of global slack. Therefore, this study was conducted to examine the effect of global slack on domestic inflation dynamics in ASEAN-5 countries by considering the effect of energy price index and commodity price index (agriculture, metals, and minerals) on domestic inflation. The data analysis method used is the Panel of Vector Error Correction Model (VECM-Panel) by considering the effect of energy price index and commodity price index (agriculture, metals, and minerals) on domestic inflation in ASEAN-5. The results show that there is an influence of global slack on domestic inflation in ASEAN-5 countries, especially through the energy price index and commodity price index. Energy prices affect inflation in both the short and long run, whereas commodity prices only affect long-term inflation. The implication of the results of this study is the importance of the role of global commodity price stability policy in maintaining domestic price stability in ASEAN-5.

Baseline scenario for the long-term development of the national industry until 2035

The article formulates the assumptions for the baseline scenario for the development of Ukrainian industry as to the dynamics of the world economy, world food and commodity price indices, the population size of Ukraine, the exchange rate of hryvnia against the dollar and the euro, the dynamics of wages in Ukraine and the EU, the propensity to invest in the Ukrainian economy, the value of the world GDP deflator. The initial data for the base scenario for the development of Ukrainian industry in 2020-2035 have been determined. The baseline scenario is conservative and reflects the continuing trends in the national industry that were observed until 2019. The main results of modeling the baseline scenario for the development of the national industry for 2020-2035 by sectors have been presented. The growth rates of value added by sectors, as well as the growth rates of GDP from 2010 to 2035 have been calculated. Changes in the share of sectors of the economy of Ukraine for 2011-2020 (fact) and from 2021 to 2035 (basic scenario) have been presented. It should be noted that the development of the scenarios was based on the continuation of the trends of 2010-2019 without taking into account unpredictable factors (such as the coronavirus pandemic or military combat), so the main goal is to assess general economic trends and factors rather than accurately predict certain indicators in specific conditions. The dynamics of relative indicators that indicate the structural features of the process of value added creation was calculated and analyzed, including capital-labor ratio, capital productivity, efficiency of labor costs and labor productivity for each sector. It has been established that in the baseline scenario, the economy of Ukraine will experience steady growth in the main sectors: mining, by an average of 1% per year; agriculture by 2-2.5% per year, while the largest growth will be observed in the processing industry, above 4% per year. This is generally in line with the trends in the global economy: growth is mainly concentrated in processing industries rather than industries that extract natural resources. The annual GDP growth, according to the simulation results, will be in the range of 3-5%, which approximately corresponds to the forecasts of various world institutions.

Impact of the war in Ukraine on the global stock market

This article focuses on and analyzes how the full-scale invasion of Russia on the territory of Ukraine on February 24, 2022, affected and continues to affect the part of the global capital market where securities are issued, bought and sold, namely the stock market. It is determined what any price changes are based on here; how stock markets can reflect the expectations of a certain group of people, in this case investors around the world, during a period of war; sanctions and pressure on the stock market. Also, the average response of twenty companies included in the Top-500 best stocks in the Center for Research in Security Prices database to percentage changes in the weighted commodity price index was evaluated. That is, we estimated how much a 1% change in the commodity price index would change the share prices of each of the 20 companies. It is determined separately how the military conflict affected the securities of American companies operating in various industries; for whom the war helped to increase capitalization, and for whom it contributed to the deterioration of the general financial condition. How the ranking of the best-performing S&P 500 stocks has changed in 2022. Why on September 13, the share prices of many global companies decreased, and the three main stock indexes (Dow Jones Industrial Average, Nasdaq Composite and S&P 500) had their worst day since June 2020. Forecast of their further changes. Separately, in addition to the American one, the changes that took place in the stock markets of Eastern countries, using the example of China, Thailand, India and Indonesia, are highlighted. Expected changes in the Russian stock market and the possibility of its complete disappearance due to sanctions imposed by various countries of the world are described. The situation on the market of Ukraine itself and the options of what exactly to invest in for domestic investors during the period of active military operations were considered. In addition to all this, the solution to problems related to the volatility of the stock market is highlighted. Thus, the question of the influence of the war between Ukraine and Russia on the state of the world stock markets, in modern realities, is paid a lot of attention not only by Ukrainian scientists, but also by foreign ones. This is not surprising, since it is impossible to ignore that the recent events have caused a chain effect, affecting firstly the prices of goods, the volume of exports, and subsequently the capitalization and growth or decline of the prices of securities of companies around the world.

Performance of different models in iron ore price prediction during the time of commodity price spike

Future prediction of commodity price based on available data is very important for mining investors and operators. Commodity prices cointegrate and show Granger causality to and from one another. This research reviewed five different estimation techniques which are Bivariate Non-Linear Regression (BNLR), Multiple Linear Regression (MLR), Multiple Non-Linear Regression (MNLR) as well as logsig and tansig model of Levenberg-Marquardt Artificial Neural Network modelling to simulate the future iron ore price based on 12 other monthly commodity prices and indices including LNG, aluminium, nickel, silver, Australian coal, zinc, gold, oil, tin, copper, lead, and Commodity Price Index (Metals).Six different models were tested in the paper to forecast the iron ore prices from 1 to 6 months over 10 months period. Linear model (purelin) using Levenberg-Marquardt technique was able to exhibit the best forecast result with average accuracy of 5.92% for 1 month ahead, 9.48% for 2 months, 11.21% for 3 months, etc. It is important to highlight that high accuracy is achieved (accuracy under 5% between forecasts and actuals in 40–50% cases) by purelin model for up to 2 months forecast for the period between July 2020 and April 2021. This indicates that prediction of iron ore price for the coming month is possible for up to 2 months period using the purelin model. It can be noted that the period tested was unstable for iron ore prices where rapid surge in iron ore price was observed. Same principle can be applied in the time of next commodity price cycle.

Dampak pandemi COVID-19 terhadap harga minyak dan pangan dunia: Analisis VECM

The study investigates the impacts of the COVID-19 pandemic on global oil price, global food price index, meat, and sugar commodity price index using vector error correction (VECM) model covering the sample period of 1st April 2020 to 31st August 2021. Data were collected mainly from WHO, FAO, and Macro Trend Websites. The result from VECM model indicates a strong cointegration relationship among the variables. In the short run, global oil price, meat price index, and sugar price index are negatively and significantly related to the COVID-19 pandemic. In the long run, the prices of these variables do not follow to the others, but there is negative reaction of global oil prices to meat and sugar price index. The COVID-19 pandemic increased the meat and sugar price index, but not significantly at the 5% level.

Russia’s invasion of Ukraine increased the synchronisation of global commodity prices

AbstractThe Russian Federation’s efforts to expand its regional political influence culminated in launching a full‐scale war of aggression on Ukraine on 24 February 2022. As both countries are large exporters of commodities crucial for global food and energy security, the resulting abrupt supply chains disruptions created substantial uncertainty in commodity markets worldwide. This study quantifies to what extent this major shock induced global commodity prices to move more synchronously by gauging their time‐varying comovement. Using the concordance index, it analyses the development of 15 key global commodity price indices from January 2010 to July 2022. We find that the supply chains disruptions increased synchronisation of grain, energy and fertiliser prices at the global level in direction and magnitude. Moreover, they resulted in contagion across numerous food and non‐food markets, creating a global covariate shock to food and energy security. Notably, the increased synchronisation at broad scale restricts the ability of consumers to mitigate the adverse effects of food and energy price inflation by resorting to inexpensive alternatives. Hence, policymakers must improve the resilience of global food supply chains sustainably such that adverse effects of attaining the Sustainable Development Goals in crises can be minimised.

Currency crises in emerging countries: The commodity factor

In this paper, we explore whether falls in commodity prices can explain the simultaneous occurrence of currency crises in emerging and developing countries. For our empirical analysis, we use a panel of 104 emerging and developing countries, covering the period 1970–2018. Our empirical investigation starts with an event study analysis, which reveals that currency crises in commodity dependent countries are preceded by commodity price growth 2 to 4 percentage points below normal. A second analysis, inspired by the literature on early warning systems, confirms this findings by showing that commodity price fluctuations are a key predictor of currency crises in commodity dependent countries. In addition, using Poisson regression analysis, we find that a 10% decrease in global commodity price indices leads to a rise of about 7% in the number of currency crises hitting commodity exporting countries.