Co-movement Of Commodity Prices Research Articles

The Co-Movement Between Commodity Prices and the Economic Growth of Malaysia

AbstractResearch Originality: This research is an initial attempt to unveil the structure of the dynamic relationship between commodity prices and the economic growth of Malaysia at various frequencies and moments in time.Research Objectives: This paper investigates the co-movement and lead-lag relationship between commodity prices and the economic growth of Malaysia at different frequencies and moments in time. Research Methods: The relationship between the variables is studied based on monthly data from 2015 to 2022 using the time-frequency analysis of Continuous Wavelet Transformation model. This method analyses the direction and lead-lag relationship between two time series to determine which variables are leading and the direction of their co-movement. Empirical Result: The relationships between commodity prices and economic growth are varied depending on frequency and moments in time. In the short run, most commodity prices exhibit an inverse relationship led by economic growth. Meanwhile, a positive co-movement with economic growth is indicated for most commodity prices in the medium run. These variables mostly move in unison, except for rubber and palm oil, which led the relationship. Nevertheless, the co-movement of commodity prices and economic growth showed a diminishing magnitude in the long run.Implications: Malaysia's policy frameworks should consider the economic dynamic responses towards commodity prices in facilitating its economic growth. In addition, efforts should be made to diversify economic activities, reducing the level of dependence on commodities in the country’s trade basket.JEL Classification: Q32, O44, O11How to Cite:Pinjaman, S., Taasim, S.I., Yun, W.S., & Yassin, J. (202x). The Co-Movement Between Commodity Prices and the Economic Growth of Malaysia. Etikonomi, 23(1), 11 – 26. https://doi.org/10.15408/etk.v23i1.34368.

Modeling Commodity Price Co-Movement: Building on Traditional Methods & Exploring Applications of Machine Learning Models

Modeling Commodity Price Co-Movement: Building on Traditional Methods & Exploring Applications of Machine Learning Models

Marionettes behind co-movement of commodity prices: Roles of speculative and hedging activities

Do investors' financial trading behaviors really affect commodity price fluctuations? What is their role behind the financialization of commodities? Adopting weekly data from the U.S. Commodity Futures Trading Commission (CFTC), this paper uses quantile regression to study the nonlinear influence of speculative and hedging activities on the co-movement of commodity returns. Empirical results indicate that, firstly, speculative activities enhance the co-movement while hedging activities weaken the correlation between different commodity prices on average. Moreover, both effects are strengthened when the correlation in the quantile regression increases. In addition, the coefficient change trend of sub samples before the crisis at different quantiles is opposite from that of the whole sample and the sample after the crisis, whether it is speculation or hedging. Lastly, speculative and hedging activities have significant asymmetric effects on the excess co-movement. Some new and interesting conclusions are found, with implications for investors, regulators and policymakers.

Liquidity effects on price and return co-movements in commodity futures markets

Commodity price comovement is an important research area in finance, and previous studies have investigated the determinants of price comovement using low-frequency (monthly or quarterly) macroeconomic data. In comparison, our paper attempts to scrutinize the liquidity effect on commodity prices and return movements based on daily data. Our findings contribute to the literature in three ways. First, we find significant positively correlated price movements across different commodity markets on a daily basis, and such comovement is driven by the cross-sectional liquidity spillover effect. Second, we observe that a cointegration relationship between individual commodity prices and the global price index can be established only if the liquidity effect is controlled. Finally, instantaneous daily liquidity shocks (i.e., innovation) exert a negative impact on daily commodity returns. However, liquidity shocks do not have a significant impact on monthly returns. Our findings are robust and have significant implications for macroeconomic policymaking, such as managing inflation risk.

Can Global Economic Policy Uncertainty Drive the Interdependence of Agricultural Commodity Prices? Evidence from Partial Wavelet Coherence Analysis

This paper employed wavelet coherence and partial wavelet coherence to investigate the time‐frequency effect of global economic policy uncertainty on the comovement of five agricultural commodities such as maize, oat, rice, soybean, and wheat using monthly data from January 1997 to December 2019. In general, we observed heterogeneity in comovement structures of the agricultural commodities market at different time‐frequency scales which are profound at high frequencies from the bivariate wavelet coherence. The partial wavelet coherence analysis shows that global economic policy uncertainty is a driver of agricultural commodity market connectedness. This implies that extreme changes in economic policy uncertainty have the tendency to influence commodity price comovement. This poses risk to the stability of the agricultural commodities market, which requires the policymaker’s intervention to protect against the spillover risk contagion effect in uncertain times.

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement

In 2008, wheat futures prices spiked and then crashed along with prices for other agricultural and nonagricultural commodities. Market observers offered several theories to explain this common movement, or comovement, in prices, and have proposed policies to address the perceived problem of excessive price volatility. The design of an appropriate policy response would benefit from a better understanding of the cause of the observed price movements. This study uses an econometric model to decompose observed wheat prices into a set of economic factors and measure the relative contribution of each factor to observed price changes. Findings show that market-specific shocks related to supply and demand for wheat were the dominant cause of price spikes in the three U.S. wheat futures markets. Fluctuations in the global macroeconomy associated with broadbased demand shocks were relatively less significant for wheat than for other commodities like crude oil and corn. Finally, little evidence suggests commodity index trading contributed to recent price spikes.

Commodity price co-movement: heterogeneity and the time-varying impact of fundamentals

This paper extends the topical literature on the co-movement and determinants of primary commodity prices, by considering heterogeneity in commodities and time variation in the impact of fundamentals. We account for heterogeneity by employing a dynamic hierarchical factor model, which decomposes commodities into global and sectoral factors. Using a time varying parameter factor augmented VAR model, we shock global and sector-specific factors over time. We present plausible impulse responses to demand shocks, real interest rate shocks, and to elevated risks during the global financial crisis. We also identify that materials, food and metals respond heterogeneously to these shocks.

Commodity-price comovement and global economic activity

Guided by a macroeconomic model with endogenous commodity prices, we apply a new factor-based identification strategy to decompose the historical sources of changes in commodity prices and global economic activity. The model yields a factor structure for commodity prices and identification conditions that provide an economic interpretation: one factor captures the combined contribution of shocks that affect commodity markets only through general-equilibrium forces. Applied to a cross-section of commodity prices since 1968, the theoretical restrictions are consistent with the data and yield structural interpretations of the common factors in commodity prices. Commodity-related shocks have contributed modestly to global economic fluctuations.

A key determinant of commodity price Co-movement: The role of daily market liquidity

Daily price co-movement across different commodity classes and its key determinant are investigated in this paper. Using co-integration and Granger causality analysis, we identify a common liquidity factor which drives prices of five commodities (oil, silver, gold, corn, live cattle) to move along a common trend. When the market becomes more (less) liquid, all commodity prices tend to move up (down) in the same direction. As a result, such liquidity-driven price co-movement across different commodity classes is likely to generate aggregate price shocks and amplify inflation volatility. As a practical implication of our findings, policy makers ought to be able to draw valuable lessons from monitoring daily commodity liquidity dynamics as a timely bellwether for incipient inflation and to more effectively control inflation risk.

The Relationship between Fuel and Food Prices: Methods, Outcomes, and Lessons for Commodity Price Risk Management

We review the fuel-food price linkage models of the time series, structural, and general or partial equilibrium nature with the main attention devoted to the time series literature. Our assessment is nested in the discussion of general commodity prices co-movement on one side and in the prediction of most likely development of biofuel policies and production development on the other side. We pay particular attention to financial markets relevant features of commodity price co-movement significant for price risk management. We show that indeed the introduction of significant biofuels policies around 2005 increased the price transmission between fossil fuels and food commodities with intuitively expected prevailing leading role of fuel prices over food prices and with particular price linkages dynamically evolving in time and depending on the particular market under consideration. The econometric results show that due to the policy induced trade barriers, there is no evidence of sufficiently integrated international biofuels market with the US, European and Brazilian markets and policies following separate paths.

Hedging systematic risk in the commodity market with a regime‐switching multivariate rotated generalized autoregressive conditional heteroskedasticity model

AbstractIn this paper, a regime‐switching multivariate rotated BEKK generalized autoregressive conditional heteroskedasticity (GARCH; RS‐MRBEKK) model for optimal futures hedging is proposed. The basic structure of the RS‐MRBEKK model is to rotate returns with spectral decomposition and fit the rotated returns with a Markov regime‐switching BEKK covariance structure that is computationally attractive for modeling higher‐dimensional regime‐switching GARCH dynamics. The empirical results reveal that adding additional commodity index futures to capture the commodity price comovement under regime switching improves hedging performance. The more parsimonious RS‐MRBEKK is statistically no worse than the conventional nonrotated regime‐switching BEKK, illustrating the usefulness of RS‐MRBEKK in higher‐dimensional hedging applications.

Commodity Price Comovement and Financial Speculation: The Case of Cotton

Recent booms and busts in commodity prices have generated concerns that financial speculation causes excessive commodity‐price comovement, driving prices away from levels implied by supply and demand under rational expectations. We develop a structural vector autoregression model of a commodity futures market and use it to explain two recent spikes in cotton prices. In doing so, we make two contributions to the literature on commodity price dynamics. First, we estimate the extent to which cotton price booms and busts can be attributed to comovement with other commodities. Finding such comovement would be necessary but would not be sufficient evidence to establish that broad‐based financial speculation drives commodity prices. Second, after controlling for aggregate demand and comovement, we develop a new method to point identify shocks to precautionary demand for cotton separately from shocks to current supply and demand. To do so, we use differences in volatility across time implied by the rational expectations competitive storage model. We find limited evidence that financial speculation caused cotton prices to spike in 2008 or 2011. We conclude that the 2008 price spike was driven mostly by precautionary demand for cotton, and the 2011 spike was caused by a net supply shortfall.

Futures Trading and the Excess Co-movement of Commodity Prices*

We empirically reinvestigate the issue of the excess co-movement of commodity prices initially raised in Pindyck and Rotemberg (1990). Excess co-movement appears when commodity prices remain correlated even after adjusting for the impact of fundamentals. We use recent developments in large approximate factor models to consider a richer information set and adequately model these fundamentals. We consider a set of eight unrelated commodities along with 184 real and nominal macroeconomic variables, from developed and emerging economies, from which nine factors are extracted over the 1993–2013 period. Our estimates provide evidenceof time-varying excess co-movement which is particularly high after 2007. Wefurther show that speculative intensity is a driver of the estimated excess comovement, as speculative trading is both correlated across the commodity futures markets and correlated with the futures prices. Our results can be taken as direct evidence of the significant impact of financialization on commodity-price crossmoments.

Increasing Trends in the Excess Comovement of Commodity Prices

Increasing Trends in the Excess Comovement of Commodity Prices

Predictability of structural co-movement in commodity prices: the role of technical indicators

This paper investigates the financialization and structural co-movement of several commodity futures using factor variance decomposition and predictability of technical indicators and macro variables. We find that financialization is still a dominant player in the commodity market and that recent commodity price fluctuations can be significantly and robustly forecasted by technical analyses of commodity index investments. Moreover, the co-movement of commodities is demonstrated by variance decomposition and explained as commodity index investment, which provides evidence of financialization. The overall empirical analysis reveals that technical indicators and macro variables can statistically and economically forecast the indexed investment and off-index trading, respectively, which indicates that they are suitable predictors of the commodity markets.

Financialization and the rise in co-movement of commodity prices

Between January 2000 and June 2008, the FAO food price index rose by 96%. Besides the magnitude, the price rise was remarkable for the broad range of commodities affected; prices of agriculture commodities, energy, and metals all rose and fell together. These dramatic developments coincided with a massive inflow of investment in the commodities futures market, and the rise of commodities as an investment class. In this paper, I study causal links between the increase in the co-movement between commodity prices and financialization of the commodities futures market. I extract common factors from a group of 40 commodities using the PANIC method and include it in a factor-augment VEC model along with a proxy of financialization. Results show that financialization of the commodities futures markets can explain the recent rise in co-movement between commodity prices, after accounting for macroeconomic variables.

Increasing trends in the excess comovement of commodity prices

We investigate how the excess comovement of commodity prices, that is, the correlation in commodity returns after filtering out common fundamental shocks, has changed over the past three decades by developing the smooth-transition dynamic conditional correlation model that can capture long-run trends and short-run dynamics of correlation simultaneously. Using data from 1983 to 2011, we find that significant increasing long-run trends in excess comovement have appeared since around 2000. We confirm that these increasing trends are neither an artifact of the financial crisis after the bankruptcy of Lehman Brothers in September 2008 nor the time-varying sensitivities of commodity returns to common fundamental shocks. Moreover, we find that no significant increasing trends exist in the excess comovement among off-index commodities and that the surge of global demand alone cannot explain the increasing trends. These findings provide additional evidence for the timing and scope of the recent increasing commodity–return correlations that suggest the influence of the financialization of commodity markets starting around 2000.

Increasing Trends in the Excess Comovement of Commodity Prices

In this paper, we investigate whether and how excess comovement among commodity returns i.e., correlation among commodity returns not accounted for by the common shocks of exogenous macroeconomic variables, have increased during these decades. To this end, we generalize the model of excess comovement, originated by Pindyck and Rotemberg (1990) and extended by Deb, Trivedi, and Varangis (1996), to develop the STDCC (smooth-transition dynamic conditional correlation) model that can capture long-run trends and short-run dynamics in excess comovement. Using monthly commodity returns data from 1983 to 2011, we find significant increasing long-run trends in excess comovement have appeared since around 2000 in all pairs of agricultural raw materials, beverages, metals, and oils. We then examine the possibility of non-monotonic trends, and find that in most cases, excess comovement continue to increase even after the financial crisis in 2008 and hence the increasing trends in excess comovement among commodity returns are not an artifact produced by the recent financial crisis, but the intrinsic nature of the excess comovement during the period including the post-crisis era. We also confirm that the increasing long-run trends of excess comovement are robust to the change in the sensitivities of commodity returns to common macroeconomic factors. Moreover, unlike the results above, we find no significant increasing trends in excess comovements among off-index commodity returns. Finally, we find that our results are robust for global macroeconomic shocks. That is, taking account of global macroeconomic variables, we still find significant, though a bit weaker, long-run increasing trends in commodity excess comovement. Those findings provide additional evidence for the recent debates about the increasing commodity-return correlations.

The Commodity Currency Puzzle

Commodity-exporting countries have persistently high real interest rates and currency excess returns. To explain this fact, I adapt a classic idea: labor cost disease, or the Balassa-Samuelson effect. Commodity booms raise wages in exporter countries, and thus make local goods and services less affordable, raising the cost of living (real exchange rate). Since the real exchange rate then moves procyclically with commodity prices, it inherits a commodity risk premium that resolves the puzzle. Using a rare-disaster setup, I show that a stochastic, international business cycle model, with local labor used as a factor of production, can quantitatively match observed commodity currency risk premia. The model’s predictions about the co-movement of commodity prices and exchange rates, the cross-section of risk premia, and the dynamics of labor costs, are also consistent with the data. Finally, to understand the impact of monetary policy, I build a New Keynesian, sticky-wage extension. Policy choices (for example, a credible peg) can reduce the risk premium on the real exchange rate, but at the cost of bigger output gaps.

Co-movement of Commodity Prices - Results from Dynamic Time Warping Classification

Several factors are responsible for difficulties in describing the behaviour of commodity prices. Firstly, there are numerous different categories of commodities. Secondly, some categories overlap with other categories, while others indirectly compete in the market. Thirdly, although essentially commodity prices react to changes in economic conditions or exchange rates, to a large extent these prices depend on supply disturbances. However, in recent years commodity prices co-move, and researchers, beginning with Pindyck and Rotemberg (1990), have been trying to explain this phenomenon. The objective of the article is to conduct the classification of the series of commodity prices in the pre-crisis and after-crisis periods. The results of such classification will reveal whether co-movement of commodity prices is the same in both periods. The analysis is based on monthly data from the period January 2001 to February 2014. All prices and price indices are published by the World Bank. The results obtained in dynamic time warping clustering reveal that co-movement of commodity prices is more evident in the pre-crisis period. There are only several paths which determine commodity prices.