This paper empirically examines the dynamic relationship between stock market volatility and commodity prices through the time-varying risk aversion channel using daily data between December 31 in 1999 and June 14 in 2021. We employ a time-varying structural-form vector autoregressive model (VAR) model with (aggregate, sectoral and sixteen individual) commodity prices. The results suggest that the transmission mechanism of stock market volatility shocks on the commodity prices change over time. The negative effect of stock market volatility on commodity prices is more statistically significant in the 2008–09 Global Financial Crisis than that during the COVID-19 pandemic in 2020. Further, the effect is greater in energy commodities compared to the agricultural and metals markets. The long-lasting negative effect of risk aversion is stronger compared to that of the expected stock market volatility on the commodity price. The change in the stock-commodity transmission mechanism is likely due to changes in underlying sources of risk aversion and expected uncertainty over time.

Food price increases stem from economic, agricultural, and political factors. Understanding the dynamics behind the food price formation process and assessing how potential factors contribute to food price changes will significantly affect policies formulated to manage food price increases. High food inflation rates have been a chronic problem in Turkey over the last decade, with unprocessed food prices rising faster than general price levels. In this article, we use exploratory analyses based on economic principles rather than econometric analyses. First, our results indicate that exchange rates are strongly associated with domestic food prices due to dependence on imported inputs. Second, deep-dive analyses on select products show that global price movements and pass-through prices from producer to consumer are not solely responsible for price increases.

This paper emphasizes the use of data-driven techniques to discern trends in commodity prices. Whereas many previous papers rely on parametric assumptions, we take as our departure point the view that such trends are inherently nonparametric, driven by complex forces of innovation and obsolescence, competition and strategic behavior, resource depletion and discovery, and supply and demand. Our reference specification is the partial linear model yt = f(t) + ztβ + ϵt where macroeconomic variables z enter parametrically, and f is nonparametric, to be discovered using cross-validation.

We analyze data on 11 commodities—3 hydrocarbons and 8 metals. For the majority of these commodities, our data-driven estimates of f bear close similarity to band pass estimates which include long term trends and super cycles. The OPEC effect is estimated to have increased oil prices by over 50% on average and coal prices by about 25%. U.S. coal mining legislation is estimated to have increased coal prices by 9% to 15%.

This article examines the relationship between Federal Reserve monetary policy and other macroeconomic indicators to both a broad commodity price index and an agricultural commodity price index by employing a vector error correction model. Excessive liquidity and the recent long period of ultralow interest rates appear to have played a statistically significant role in affecting prices in the commodities markets. The responses of commodity prices to monetary policy that we estimate generally conform to earlier findings, but the sensitivity of the responses appears different in the face of the unprecedented scope of recent Fed activism.

The relationship between complete-feed prices and ingredient prices is estimated in order to analyze the effect of higher commodity prices on feed costs, with particular attention paid to the substitutability of corn distillers dried grains with solubles (DDGS). Using the historical price correlation between corn and DDGS, each $1 per ton increase in the price of corn increases feed costs between $0.45 and $0.59 per ton across livestock sectors. Marginal feed costs based on lower forecasted price correlations are reduced between $0.05 to $0.12 per ton across livestock sectors, but only for the dairy ration is the reduction statistically significant. Overall, DDGS cost savings are relatively limited and insufficient to offset the impact of other higher-priced feedstocks.

Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

Exchange rates have long been thought to have an important impact on the export and import of goods and services, and, thus, exchange rates are expected to influence the price of those products that are traded. At the same time, energy impacts commodity production in some very important ways. The use of chemical and petroleum derived inputs has increased in agriculture over time; the prices of these critical inputs, then, would be expected to alter supply, and, therefore, the prices of commodities using these inputs. Also, agricultural commodities have been increasingly used to produce energy, thereby leading to an expectation of a linkage between energy and commodity markets. In this paper, we examine the price relationship through time of the primary agricultural commodities, exchange rates, and oil prices. Using overlapping time periods, we examine the cointegration relationship between prices to determine changes in the strength of the linkage between markets through time. In general, we find that commodity prices are linked to oil for corn, cotton, and soybeans, but not for wheat, and that exchange rates do play a role in the linkage of prices over time.

This paper explores empirically the causes of extreme fluctuations in commodity prices from January 1990 to June 2010 and seeks to identify the relative contribution of advanced and emerging market economies to the changes in commodity prices. Our assumption is that analyzing two very distinct goods—crude oil and fine wine—helps to identify common determinants of commodity prices. We find that the growth rate of global aggregate demand is the key macroeconomic determinant of the fluctuations in both crude oil and fine wine prices over the sample period. While advanced economies account for more than half of global consumption, emerging market and developing economies make up the bulk of the incremental change in demand, thereby having a greater weight in commodity price formation. The coefficient of emerging market industrial output growth is about three times as high as that of advanced economies in oil price regressions and almost five times as powerful in fine wine price regressions. The results also show that the shift in the composition of aggregate commodity demand is a recent phenomenon. (JEL Classifications: Q11, Q39, Q41, Q43)

Tropical deforestation is one of the most important components of global change. Rates of deforestation in Brazil, the nation with the single largest concentration of tropical forest on Earth, have fluctuated widely over the last twenty years. Based on local knowledge, such fluctuations have been variously attributed to a wide range of factors such as the expansion of cattle ranching and soybean farming, infrastructural expansion and the proliferation of paved and unpaved roads, macroeconomic shocks to the Brazilian economy and international exchange rates. Many, if not all, of these arguments are plausible explanations for temporal variation in deforestation rates, but have to date not been subjected to rigorous statistical testing; this study investigates the potential impact of these variables on Brazilian tropical deforestation over the period 1990–2005. When analysed at the basin-wide scale, nearly all variables were highly inter-correlated through time and were also closely correlated with deforestation rate, but appropriate time-series analysis found no statistical evidence that any of the variables have systematically caused variation in deforestation rates. Power analysis showed that the variables may exert small or medium influences on deforestation rates, but the impacts, if present, are not strong. Future analyses of time series data at finer spatial scales that exploit spatiotemporal variation in deforestation rates and in the hypothesized predictor variables may find significant causal processes that are overlooked when analysed at the basin-wide scale.