Commodity Price Index Research Articles

Investigating the Impact of COVID-19 Outbreak on Canada's Commodity Market

SARS-Cov-2 was first reported in Wuhan, a town in Hubei Province of China with a population of 11 million in December 2019, following an outbreak of non-pneumonia of a clear cause. The virus has now spread across the globe considerably more than 200 countries and territories, and the world health organization (WHO) described it as a pandemic on 11 March 2020. On the economic front, COVID-19 has led more than 200 countries partially or totally lockdown. This situation disrupted global supply chain, and induced a significant fall in both economic activity and financial asset prices. Canada, being one of the country affected by the Virus with 731 000 Cases, and 18 622 Deaths as at the 21 of January 2020. Recently vaccines have been developed but the contamination wave keeps on increasing every day. On the socioeconomic front, COVID-19 has led more than 200 countries into partial or total lockdown, disrupted global supply chains, and induced a fall in both economic activity and financial asset prices. It’s of prime importance to study how COVID-19 impact’s on Canada’s commodity market. The commercial and financial effect of COVID-19. The impact of COVID-19 is unclear because the public health crisis is still unfolding. There is limited amount of studies concerning this topic, because the crisis is still unfolding. The objective of this study is to investigate the impact of Covid-19 on Canada’s Commodity markets. The author uses extreme bound analysis for market interpretation and sourced data from Bank of Canada and Our World in Data COVID-19, from January 1 to December 31 2020. The result of the study show that, Total deaths per million have an impact on the monthly Bank of Canada’s commodity price index of energy because of the robust relationship between them. Furthermore, the result also show that, commodity price index of energy and total deaths per million are determinants to total commodity price index. Furthermore, commodity price index excluding energy, commodity price index of energy, commodity price index of minerals and metals, commodity price index of forestry and total deaths per million are robust determinants of monthly Bank of Canada total commodity Prices. M.BCNE, M.ENER, M.MTLS, M.MTLS, M.FOPR, M.FOPR, M.AGRI, M.FISH, TC, TD have a positive relationship with MBCPI. M.BCPI, M.BCNE, M.MTLS, M.AGRI and M.FISH.

Research on the influence of US QE policy on Sino-US trade

This article selects the major international commodity price index, the RMB against the dollar, foreign direct investment in China, the broad money supply of US, China's exports to the US and China’s imports from the US of monthly data from January 2020 to June 2021, using multiple linear regression model to study how the quantitative easing monetary policy of US impact on Sino-US trade during the outbreak of the COVID-19. The empirical results show that international commodity price index has a negative correlation with China's exports to the US and imports from US, the RMB against the dollar has a negative impact to China’s imports from US, the broad money supply of US has a significant positive impact on both China’s export to US and import from US and FDI has a positive impact to China’s imports from US to some extent.

Seven centuries of commodity co-movement: a wavelet analysis approach

ABSTRACT We examine interdependencies between agricultural, industrial, and energy commodity price indices suing data sets from more than seven centuries. To this end, we apply wavelet coherence and wavelet phase difference. There is a high coherence between prices for all three commodity-groups with energy prices now leading both agricultural and industrial commodity prices. Also, the role of energy as a leading series has increased over time.

Is the agricultural sector cursed too? Evidence from Sub-Saharan Africa

• We investigate the direct and indirect effects of extractive resources on the agricultural sector in Sub-Saharan Africa. • We find that resource windfalls decrease the level of agricultural labor productivity. • We find that modernization in agriculture can possibly be altered by the lack of spillovers from the manufacturing sector. • Modernization can also possibly be impacted by the low level of productive investment conditional on the quality of institutions. • We confirm that a high level of agricultural labor productivity is positively correlated with the release of labor towards non-agriculture. Extractive and agricultural resources do not have the same impact on poverty reduction and can compete with each other. We examine how extractive resource windfalls affect agricultural productivity, measured as the amount of output per worker in the agricultural sector. This is important since agricultural productivity is a key element of structural transformation and poverty reduction. To do this, we exploit a panel dataset of 38 countries over 1991-2016 and construct a country-specific commodity price index that captures resource-related gains and losses in aggregate disposable income. We find that an increase in the commodity price index leads to a drop in agricultural productivity in Sub-Saharan economies. Among the possible mechanisms to explain this result, our findings highlight the lack of spillovers across sectors and the low level of agricultural investment in autocratic regimes, both related to the exploitation of extractive resources. We also find that higher agricultural productivity is positively associated with the release of workers towards manufacturing and services, confirming its importance for structural transformation.

Interaksi Faktor Eksternal dengan Siklus Finansial di Indonesia

This study aims to interact global financial market fluctuations, commodity price fluctuations and capital flows to determine which factors interact with the cycle in Indonesia the most. The data used is secondary data from official publications, namely Bank Indonesia for data on capital flows and bank credit, the International Monetary Fund (IMF) for international commodity price index data and the global financial market valatility index (VIX). The analysis period in this study is the first quarter of 1993 to the fourth quarter of 2018. The results of the study using the correlation index show that the cycle of capital flows in Indonesia is carried out by global finance and The movement of commodity prices causes the flow of funds that enter the Indonesian economy to be largely short-term, so it tends to have acyclical interactions with bank credit to the business world and is slightly more procyclical for credit to non-business fields. Credit to non-business fields is dominated by bank credit, which is allocated to consumption credit and has a short term.

Are commodity prices good predictors of inflation? The African perspective

This paper examines the predictive ability of commodity prices in forecasting inflation in two largest African economies. We consider an augmented Phillips curve model with symmetric and asymmetric commodity prices to forecast headline and core inflation using different methodological approaches. We found evidence of persistency, endogeneity and conditional heteroscedasticity in the predictors; thus, we apply the Westerlund and Narayan (2015) estimator which allow for these features in the predictive model. Consequently, we extend our model to account for asymmetries in commodity price changes. Our results show that commodity price indexes are good predictors for both headline and core inflation in both Nigeria and South Africa. Therefore, amidst all the competing multi-predictor framework, commodity prices outperform all other variants both in the in-sample and out-of-sample forecasts.

Commodity price volatility and the economic uncertainty of pandemics

We empirically investigate the impact of economic uncertainty related to global pandemics on the volatility of the broad commodity price index as well as on the sub-indexes of crude oil and gold. The results show that uncertainty related to pandemics have a strong negative impact on the volatility of commodity markets and especially on crude oil market, while the effect on gold market is positive but less significant.

The relationship between commodity prices and world trade uncertainty

This paper investigates the relationship between commodity prices and world trade uncertainty for the period of 1996Q1–2019Q3. World Trade Uncertainty Index (WTU), developed by Ahir et. al. (2018), and Commodity Price Index for the United States of America (USA) have been used in the analyses. For analyzing this relationship Continuous Wavelet Transform (CWT), Wavelet Coherency (WTC), and Wavelet Power Spectrum (WPS) techniques have been applied in the paper. Results indicate that there are strong noises for WTU between 2009–2010 and 2015–2016; for CPI between 2008–2009 and 2015–2016 and between 2008–2009 and 2017–2018 with a positively correlated and significant causal prediction.

Fed’s unconventional monetary policy and risk spillover in the US financial markets

This study examines volatility spillover dynamics among the S&P 500 index, the US 10-year Treasury yield, the US dollar index futures, and the commodity price index. The focus of the study is to analyze the effects of the Fed’s unconventional monetary policy on the US financial markets. We use realized volatility measures based on daily data covering the period from December 29, 1996, to November 12, 2018. To address nonlinear and asymmetric spillover dynamics in low and high volatility states, we propose a new regime-dependent spillover index based on a smooth transition vector autoregressive (STVAR) model, extending the study of Diebold and Yilmaz (2008, 2012) to regime switching models. When applied to US financial data, we find strong evidence that the US financial market risk structure changes after the 2008 Global Financial Crisis and announcement of quantitative easing (QE) programmes through the portfolio balance channel. The risk spillover moves from purchased assets to non-purchased assets after the QE announcements.

Macroeconomic effects of macroprudential policies: Evidence from LTV and DTI policies in Korea

This study investigates the effects of loan-to-value (LTV) and debt-to-income (DTI) policies on macroeconomic variables such as commodity price index (CPI) and industrial production (IP) and financial variables such as house price and household bank loan in Korea by employing a structural vector autoregression (VAR) model. We use measures of LTV and DTI regulations that properly reflect changes in regulation coverage and intensity. Empirical results show that LTV and DTI shocks have significant effects not only on house price and household bank loan but also on CPI and IP, particularly when both policies are implemented together. The effects of DTI shocks are similar to those of monetary policy shocks, but LTV and DTI shocks tend to have a slower effect on CPI and IP but a faster effect on house price and household bank loan.

Some Empirical Models of Japanese Government Bond Yields Using Daily Data

This paper models the dynamics of Japanese government bond (JGB) nominal yields using daily data. Models of government bond yields based on daily data, such as those presented in this paper, can be useful not only to investors and market analysts, but also to central bankers and other policymakers for assessing financial conditions and macroeconomic developments in real time. The paper shows that long-term JGB nominal yields can be modeled using the short-term interest rate on Treasury bills, the equity index, the exchange rate, commodity price index, and other key financial variables.

Energy price shocks and dynamics of current account in Turkey: a FAVAR Approach

Purpose- In this article, we examine the linkages between current account and energy prices, namely crude oil, coal and natural gas in Turkey the years between 2005 and 2016. Methodology- This article investigates how the changes in the energy prices affect the current account in Turkey. For this purpose, a Factor-Augmented Vector Auto Regression (FAVAR) model is used with economic data from the Turkish economy and world commodity price indexes to obtain empirical results for current account dynamics in the Turkish economy. The impact of various factors that include crude oil prices, coal prices, natural gas prices and fuel (Energy) index (Crude oil, natural gas, and coal price indices) data have been studied by obtaining impulse response functions. Findings- Natural gas prices gradually affect the current account. The impact is permanent after a period indicating a very limited elasticity. Oil prices have an immediate The current on the current account. Cycles, in the long run, indicate some degree of elasticity. The negative effect of coal price on the current account deficit occurs with a significant delay. Crude oil price index shocks graphs and fuel index, which includes oil price, coal price and natural gas, and crude oil price shocks graphs are very similar. Conclusion- In this comprehensive study, we can conclude that crude oil prices, coal prices, natural gas prices deteriorate current account balance. From all of the energy indices, the crude oil price is the most effective on the current account.

Energy price shocks and dynamics of current account in Turkey: is this time different

Purpose- In this article, we examine the link between current account and energy prices, namely crude oil, coal and natural gas in Turkey the years between 2005 and 2016 . Methodology- This article investigates how the changes in the energy prices affect the current account in Turkey. For this purpose, a Factor-Augmented Vector Auto Regression (FAVAR) model is used with economic data from the Turkish economy and world commodity price indexes to obtain empirical results for current account dynamics in the Turkish economy. The impact of various factors that include crude oil prices, coal prices, natural gas prices data have been studied by obtaining impulse response functions. Findings- Natural gas prices gradually affect the current account. The impact is permanent after a period indicating a very limited elasticity. Oil prices have an immediate impact on the current account. Swings, in the long run, indicate some degree of elasticity. The negative effect of coal price on the current account deficit occurs with a significant delay. Conclusion- We can conclude that, from all of the energy indices, the crude oil price is the most effective on the current account.

Recourse loans and Ponzi schemes

Non-recourse borrowing leaves no room for Ponzi schemes, as shown by Araujo et al. (Econometrica 70:1613–1638, 2002). This is not the case with recourse loans, for which, in the event of default and on top of the foreclosure of the collateral, the debtor’s estate can be seized or (in a way common in the GE literature) the debtor can suffer utility penalties. We focus on the latter and show that infinite horizon equilibrium with recourse exists in some interesting cases: (1) if utility penalties are low enough and the collateral does not yield utility (for example, when it is a productive asset or a security) or (2) for a nominal promise backed by real collateral (such as mortgages, whose payments are not tied to a commodity price index).

Can energy commodities affect energy blockchain-based cryptos?

PurposeThe purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market.Design/methodology/approachUsing principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices.FindingsNatural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model.Research limitations/implicationsSignificant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil.Originality/valueAs per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries

In this paper, we offer new evidence on the predictability of exchange rate with commodity prices using the Westerlund and Narayan (2015) estimator. The estimator allows us to control for any possible bias in the estimation process due to the presence of persistence, endogeneity and conditional heteroscedasticity effects in our predictors. We further account for the role of asymmetries and structural breaks both for the in-sample and out-of-sample forecasts. In particular, we evaluate whether such considerations matter for the forecast performance of the predictive model for exchange rate. Monthly data of five major tradable currency pairs in the world and disaggregated commodity price indices over the period of 1960–2017 are utilized. We find significant improvements in both the in-sample and out-of-sample forecast performance of the predictive model for exchange rate using this estimator and particularly when asymmetries and structural breaks are accommodated. While the results are robust to multiple currency pairs, commodity price indices, breaks and forecast horizons, however, the choice of data frequency has implications on the forecast outcome. Essentially, the forecast accuracy of the proposed predictive model seems to diminish with lower data frequencies.

Push vs. Pull Factors of Capital Flows Revisited: A Cross-country Analysis

Capital market integration contributes to economic growth and it can be more beneficial for emerging market economies (EMEs, hereafter) at their early stages of development where the capital is relatively insufficient. An open capital market also enables investor to share the country-specific risks by holding foreign assets. However, there are also some negative side effects of capital market integration. Financial shocks originating in the center country can be quickly propagated through the integrated financial market. The Global Financial Crisis (GFC, hereafter) is a good example of the contagion of the financial crisis. Volatile cross-border capital inflows and outflows negatively affect financial stability, which eventually lowers economic growth by causing financial crises. Despite of these negative side effects, capital market integration has been an inevitable long-term trend for many EMEs over the past few decades (Aizenman et al. 2010). There have been continuous capital flows to EMEs, which started even before GFC and this trend has been more pronounced during the U.S. zero-interest rate period (Ahmed and Zlate 2014). Though some monetary authorities in EMEs tried to moderate the procyclicality of credit flows by implementing policy instruments such as capital controls or macro-prudential policy measures after GFC (Kim and Mehrotra 2018), the common factors in the global financial market still play a crucial role in determining capital inflows to EMEs. The relationship between the global financial condition and its impact on capital inflows to EMEs, has been a long-debated issue. This issue concerns whether push or pull factors are the major determinant of capital flows. The push factor represents the common factor that exists in the global financial market or center countries, which influences capital inflows to peripheral countries. These factors are interest rates and GDP growth rates of advanced economies (AEs, hereafter), global risk factors such as VIX (S&P 500 Volatility Index), and the commodity price index. The pull factor denotes domestic factors that attract funds from the global financial market to domestic financial markets. These factors are domestic interest rates, domestic GDP growth rates, and other country-specific characteristics such as exchange rate regime, degree of the capital account openness, institutional quality, and stages of economic development. In previous literature, many scholars have found strong evidence for push factors being the major determinant of capital movement. The interest rates of mature economies and VIX are significant determinants of capital inflows to EMEs. However, there is only some evidence that higher domestic interest rates and higher domestic GDP growth rates pull capital from the center countries to individual EMEs (Koepke 2015). Related to this long-debated issue in academia, the Chairman of the Federal Reserve, Jerome H. Powell recently stated, ... I will argue that, while global factors play an important role in influencing domestic financial conditions, the role of U.S. monetary policy is often exaggerated. With this statement, he also pointed out that the slowdown in capital inflows to EMEs which has been happening ever since 2011 has been mainly due to the narrowing of GDP gaps between AEs and EMEs, i.e., the recent decrease in capital in-flows to EMEs can be attributed to the decline in EMEs' GDP growth rates given the fact that the U.S. GDP growth rate has picked up. In this paper, we revisit this issue of push and pull factors of capital inflows. To this end, we consider the heterogeneity that exists in EMEs by dividing them into four subgroups. We investigate which is the main driver of capital inflows between push and pull factors across country groups. Categorizing subgroups is important for two reasons. First, EMEs are so heterogeneous that we make subgroups which share similar economic fundamentals by regions. Second, making subgroups across EMEs is an effective way to indirectly consider the regional contagion effect. With this cross-country analysis, we can figure out the differing effects of push and pull factors across country groups, and this can eventually lead to the development and implementation of appropriate policy instruments. Our empirical finding shows that the push and pull factors play a different role in determining capital inflows to AEs and EMEs. The major drivers of capital inflows to AEs are both push and pull factors, but push factors turn out to be the main determinant of capital inflows to EMEs. When EMEs are divided into four subgroups, we find sizable heterogeneity across subgroups. In Asian countries, both push and pull factors are significant, which is similar to AEs, but only U.S. interest rate plays a major role in Eastern Europe. Some pull factors are important in Latin American countries and other EMEs, but these are not robust to alternative empirical models and measures.

Fed’s Unconventional Monetary Policy and Risk Spillover in the US Financial Markets

This study examines volatility spillover dynamics among the S&P 500 index, the US 10-year Treasury yield, the US dollar index futures and the commodity price index. The focus of the study is to analyze effects of Fed’s unconventional monetary policy on the US financial markets. We use realized volatility measures based on daily data covering the period from December 29, 1996 to November 12, 2018. To address nonlinear and asymmetric spillover dynamics in low and high volatility states, we propose a new regime-dependent spillover index based on a smooth transition vector autoregressive (STVAR) model, extending the study of Diebold and Yilmaz (2009,2012) to regime switching models. When applied to US financial data, we find strong evidence that the US financial market risk structure changes after the announcement of quantitively easing (QE) through the portfolio balance channel. The risk spillover moves from purchased assets to non-purchased assets after the QE announcements.

Commodity Terms of Trade

This paper presents a comprehensive database of country-specific commodity price indices for 182 economies covering the period 1962-2018. For each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level trade data. The database includes a commodity terms-of-trade index—which proxies the windfall gains and losses of income associated with changes in world prices—as well as additional country-specific series, including commodity export and import price indices. We provide indices that are constructed using, alternatively, fixed weights (based on average trade flows over several decades) and time-varying weights (which can account for time variation in the mix of commodities traded and the overall importance of commodities in economic activity). The paper also discusses the dynamics of commodity terms of trade across country groups and their influence on key macroeconomic aggregates.

Commodity Terms of Trade: A New Database

This paper presents a comprehensive database of country-specific commodity price indices for 182 economies covering the period 1962-2018. For each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level trade data. The database includes a commodity terms-of-trade index-which proxies the windfall gains and losses of income associated with changes in world prices-as well as additional country-specific series, including commodity export and import price indices. We provide indices that are constructed using, alternatively, fixed weights (based on average trade flows over several decades) and time-varying weights (which can account for time variation in the mix of commodities traded and the overall importance of commodities in economic activity). The paper also discusses the dynamics of commodity terms of trade across country groups and their influence on key macroeconomic aggregates.