Volatility Effects of Index Trading and Spillovers on US Agricultural Futures Markets: A Multivariate GARCH Approach

We examine the effects of speculation in the form of index trading on contemporaneous returns and volatility on corn, soybeans and wheat futures markets on the Chicago Board of Trade using multivariate generalised autoregressive conditional heteroscedasticity models and weekly data for 2006–2014. We also assess spillovers. Results are threefold. First, contemporaneous effects of index trading on own returns are positive and inelastic, and they are partially mitigated in the following week. Second, volatility depends positively on own past volatility, and volatility spillovers are limited. Third, index trading reduces own volatility.     

Extent and possible causes of intrayear agricultural commodity price volatility

Wheat, corn, rice, soybeans, and cotton experienced higher volatility in the second half of the 2000s. For the sample at hand, the unit root tests only validate a new period of high volatility for wheat and cotton. If in the next couple of years however, corn, rice and soybeans maintain their higher volatility, a new period of high volatility may also be validated statistically. Regarding the factors driving the intrayear volatility GMM estimates show that “commodity market fundamentals” i.e., the stock‐to‐use ratio and to a lesser extent the degree of internationalization, are the most systematically statistically significant coefficients among commodities. Over time, consecutive low stock‐to‐use ratios and a thin international market provoke typically high volatility. Speculative activity and liquidity in the agricultural derivative market have a stabilizing effect on the spot price, if any. Finally, “common macro” factors significantly impact volatility, especially the volatility of petrol and of exchange rates; their dispersion importance over the sample is quite sizeable. However, it is difficult to establish a link between, on the one hand, loose monetary policy, business cycle and inflation, and, on the other hand, commodity price volatility, as the sign of the estimated coefficient changes depending on the commodity and the estimated elasticities are quite low.     

Agricultural price transmission across space and commodities during price bubbles

This article investigates agricultural price transmission during price bubbles. The empirical approach concerns the horizontal transmission of cereal prices both across different market places and across different commodities. The trade policy intervention put forward to mitigate the impact of price exuberance is considered. The analysis is performed using Italian and international weekly spot (cash) price data over years 2006–2010, a period of generalized turbulence of agricultural markets. Firstly, the properties of price time series are explored; then, interdependence across prices is specified and estimated by adopting appropriate cointegration techniques. Results suggest that the bubble had only a slight impact on the price spread and the temporary trade‐policy measure, when effective, has limited this impact.     

A quantile autoregression analysis of price volatility in agricultural markets

This paper investigates the dynamics of agricultural price volatility based on a quantile autoregression (QAR) model. The QAR model provides a flexible representation of the distribution of price and its dynamics. The approach is applied to U.S. wheat and corn markets over the period of 1980–2017. This period is of significant interest as it covers important changes in agricultural policy and increased reliance on markets. The price analysis is conducted conditional on stocks held in the previous period. We show how increasing previous stocks shift the price distribution to the left and decreases the odds of facing price spikes (by shifting down the upper tail of the price distribution). Our analysis also examines the effects of changing public stocks on prices. For both wheat and corn, this reflects changing agricultural policy, contrasting the 1980s (when public stocks were relatively high) with the post-2005 period (when public stocks became zero). We document how higher public stock ratio during the previous period did not lower the odds of facing price spikes. Applied to the wheat and corn markets, we also uncover evidence of local dynamic instability in the upper tail of the price distribution, suggesting that price instability becomes more pronounced when previous stocks are low.     

Financial activity in agricultural futures markets: evidence from quantile regressions

This study analyses the relationship between financial activity and price returns in 12 US agricultural futures markets. It contributes to the existing research by exploring the forecasting power of trading activity for returns from the perspective of conditional quantiles. Quantile regressions detect Granger‐causal effects from positions of speculators and index traders to price returns in a wide range of commodity markets such as cocoa, coffee, corn, sugar and SRW wheat.     

Price shock transmission: evidence from the wheat‐bread market value chain in Ethiopia

This study assesses the degree of vertical price transmission along the wheat‐bread value chain in Ethiopia. This is pursued by applying a vector error correction model and an impulse response analysis using monthly price data for the period 2000–2015. Our analysis considers transmission of price shocks across different market levels, including from the international and domestic wheat grain markets at the upstream to the domestic wheat bread market at the downstream of the value chain. The empirical findings indicate that significant cointegration exists across prices of the different market stages. There is a transmission from international prices to domestic prices at downstream markets, in particular to flour and bread prices. Prices at upstream markets are largely influenced by the domestic wholesale market. In general, the speed of adjustment is quite slow with a half‐life of about one year for restoring the equilibrium price relationship. As price margins between the different market stages in the value chain have substantially decreased in the last 15 years, higher transmission, and thus exposure to international market shocks, can be expected in the future. The results also show that causal relationships exist between prices at different market stages—with the wholesale market identified as the key market level where prices and price expectations are formed.     

Monthly Projections of National Year-Ending Grain Socks Using Quarterly and Weekly Quantity Data

A method for projecting national marketing year ending stocks of corn, sorghum, soybeans and wheat on a monthly basis using quarterly and weekly quantity data is introduced and applied to 1975-1987 data. Five years of monthly rejections for wheat and soybeans and four years of projections for corn and sorghum are compared with published USDA projections. Refinement of this technique will allow for estimation of current stocks levels or projections of ending stocks levels at any point in the marketing year.     

A Statistical Analysis of Wheat Price Fluctuations in England: 1685–1850

For some commodities and time periods, the analysis of price fluctuations must necessarily rely on the existence of price data alone. A theory applicable in such circumstances is outlined for commodities that are storable, traded in open markets and subject to net supply shocks which are heterogeneously distributed across the months of the year. Market prices are predicted to vary autoregressively except at times when wheat stocks become negligible and observed market prices exceed threshold prices (which may themselves differ across months). The model is applied to a monthly time series of wheat prices for southern England from 1685 to 1850. The autoregressive parameter and the threshold prices are estimated, substantial empirical support being found for the models tested. Historical events from the late seventeenth century through to the continental wars in the late eighteenth and early nineteenth centuries are used to illustrate the mechanisms underlying the theory.     

Evaluating price transmission between global agricultural markets and consumer food price indices in the European Union

This paper uses error correction models to evaluate the extent to and speed at which world agricultural commodity price movements affect consumer food prices in the European Union member states. We consider three types of world commodity price indices, each containing different commodities and weighting criteria. Results reveal a long‐run relationship between world agricultural commodity and consumer food prices in over half of the member states. Consumer prices in different member states and categories of member states respond differently to specific world price indices, suggesting that there are disparities in the structure and the efficiency of their food markets. The eurozone founders generally have lower transmission elasticities. This should be taken into account when predicting the impacts of extreme world price volatility and consumer food price rises, prompting governments to pay attention to the most vulnerable households.     

A Price Analysis of U.S. Agricultural Exports in East Asian Markets

This paper examines the price competition between U.S. agricultural exports and that of its competitors in East Asia. The results show weak price competition in Japan's corn and soybean markets, and no price competition in the wheat market. U.S. cotton exports to Japan face strong price competition. In Hong Kong, U.S. market shares are low, while the demand for its rice, corn, soybeans and cotton is elastic. For South Korea and Taiwan, improved political relationships between China, South Korea, and Taiwan have caused the U.S. to lose market shares to China.     

SPECTRAL ANALYSIS OF THE THEORY OF ANTICIPATORY PRICES

The paper investigates the intertemporal relationship between cash and futures price changes using the techniques of spectral analysis. Daily data for the wheat, corn and soybean markets are analysed to determine if and how cash and futures prices move together. On a daily basis wheat and corn cash and futures prices move together; soybeans cash and futures prices do not move together on a daily basis.     

Inter‐ and intra‐seasonal crop acreage response to international food prices and implications of volatility

Understanding how producers make decisions to allot acreage among crops and how decisions about land use are affected by changes in prices and their volatility is fundamental for predicting the supply of staple crops and, hence, assessing the global food supply situation. This study makes estimations of monthly (i.e., seasonal) versus annual global acreage response models for the world's principal staple food crops: wheat, corn, soybeans, and rice. Primary emphasis is given to the magnitude and speed of the allocation process. Estimation of intra‐annual acreage elasticity is crucial for expected food supply and for input demand, especially in the light of the recent short‐term volatility in food prices. The econometric results indicate that global crop acreage responds to crop prices and price risks, input costs as well as a time trend. Depending on respective crop, short‐run elasticities are about 0.05 to 0.40; price volatility tends to reduce acreage for some of the crops; comparison of the annual and the monthly acreage response elasticities suggests that acreage adjusts seasonally around the globe to new information and expectations. Given the seasonality of agriculture, time is of an essence for acreage response. The analysis indicates that acreage allocation is more sensitive to prices in the northern hemisphere spring than in winter and the response varies across months.     

Assessing the Vulnerability to Price Spikes in Agricultural Commodity Markets

We examine empirically the predictability of conditions associated with a higher probability of a price spike in agricultural commodity markets. We find that the forward spread is the most significant indicator of probable price jumps in maize, wheat and soybeans futures markets, a result which is in line with the ‘Theory of Storage’. We additionally show that some option-implied variables add significant predictive power when added to the more standard information variable set. Overall, the estimated probabilities of large price increases from our probit models exhibit significant correlations with historical sudden market upheavals in agricultural markets.     

Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets

Over recent decades, the link between crude oil and agricultural markets has been reinforced following the introduction of biofuels. We use timely measures of (co)variation spillovers to analyze the role of crude oil in shaping price uncertainties of agricultural commodities, which are largely used as biofuel feedstocks. Our sectoral- and market-specific measures distinguish tranquil (1995–2005) and crisis episodes (2006–2015), as well as periods during which either consumption mandates or tax credits were enacted to spur biofuels. During the crisis period, crude oil volatility transmissions account for 16% (20%) of price uncertainties in ethanol (biodiesel) feedstock markets on average. Moreover, we find evidence of enhanced volatility transmissions under tax credit regimes compared with consumption mandates. The results from pooled regressions confirm stronger volatility transmissions by about 12% under the enactment of tax credits.     

Modeling regime‐dependent agricultural commodity price volatilities

In stark contrast to financial markets, relatively little attention has been given to modeling agricultural commodity price volatility. In recent years, numerous methodologies with various strengths have been proposed for modeling price volatility in financial markets. We propose using a mixture of normals with unique GARCH processes in each component for modeling agricultural commodity prices. While a normal mixture model is quite flexible and allows for time varying skewness and kurtosis, its biggest strength is that each component can be viewed as a different market regime and thus estimated parameters are more readily interpreted. We apply the proposed model to ten different agricultural commodity weekly cash prices. Both in‐sample fit and out‐of‐sample forecasting tests confirm that the two‐state NM‐GARCH approach performs better than the traditional normal GARCH model. A significant and state‐dependent inverse leverage effect is detected only for pork in the regime where the price is expected to drop, indicating the volatility in this regime tends to increase more following a realized price rise than a realized price drop.     

价格支持、政策调整及粮食市场波动

目的 为了估计价格支持政策对不同粮食品种期现货价格波动的直接影响,实证分析和比较了政策及其调整对粮食期现货价格波动实施效果的影响,为深化粮食价格形成机制改革提供一定的理论参考和实证支撑。方法 文章利用稻谷、小麦、玉米和大豆的现货与期货价格日数据,将政策以虚拟变量的形式引入GARCH模型实证分析最低收购价政策、临时收储政策及其调整对平抑粮食期现货市场波动的作用。结果 价格支持政策对粮食价格波动产生了显著影响,最低收购价政策能够明显降低稻谷和小麦现货市场的波动程度,但对期货市场波动的作用则相反;玉米和大豆临时收储政策的取消导致现货市场波动性提高,而对期货市场波动的影响存在差异。结论 价格支持政策具有降低价格波动的作用效果,政策调控效果与实施品种的国内供求及市场形势、国内外市场的联系程度密切相关,政策的完善还需关注对期货市场波动的影响。     

Volatility Forecasting and Time‐varying Variance Risk Premiums in Grains Commodity Markets

In this paper we examine empirically the predictive power of model‐free option‐implied variance and skewness in wheat, maize and soybeans derivative markets. We find that option‐implied risk‐neutral variance outperforms historical variance as a predictor of future realised variance for these three commodities. In addition, we find that risk‐neutral option‐implied skewness significantly improves variance forecasting when added in the information variable set. Variance risk premia add significant predictive power when included as an additional factor for predicting future commodity returns.     

Using a functional approach to test trending volatility in the price of Mexican and international agricultural products

In this article, we extend the traditional GARCH(1,1) model by including a functional trend term in the conditional volatility of a time series. We derive the main properties of the model and apply it to all agricultural commodities in the Mexican CPI basket, as well as to the international prices of maize, wheat, swine, poultry, and beef products for three different time periods that implied changes in price regulations and behavior: before the North American Free Trade Agreement (NAFTA; 1987–1993), post‐NAFTA (1994–2005), and commodity supercycle (2006–2014). The proposed model seems to adequately fit the volatility process and, according to heteroscedasticity tests, also outperforms the ARCH(1) and GARCH(1,1) models, some of the most popular approaches used in the literature to analyze price volatility. Our results show that, consistent with anecdotal evidence, price volatility trends increased from the period 1987–1993 to 1994–2005. From 1994–2005 to 2006–2014, trends decreased but the persistence of volatility increased for most products, especially for international commodities. In addition, we identify some agricultural products such as avocado, beans, and chicken that, due to their increasing price volatility trends in the 2006–2014 period, may present a risk for food inflation in the short run.     

Evaluation of USDA Interval Forecasts of Corn and Soybean Prices

USDA World Agricultural Supply and Demand Estimates (WASDE) price forecasts are published as an interval, but are typically analyzed as point estimates. Thus, all information about uncertainty imbedded in the forecast is ignored. The purpose of this article is to evaluate the accuracy of WASDE corn and soybean price forecasts using methodology suitable for testing judgmental interval forecasts. Accuracy tests suggest that WASDE forecasts are not calibrated at the 95% confidence level for both commodities and generally not calibrated for corn, but calibrated for soybeans, at the implied confidence level elicited from the survey of forecast providers.     

Price and Volatility Transmission and Market Power in the German Fresh Pork Supply Chain

We investigate the relationship between the transmission of price volatility and market power in the German fresh pork supply chain. We use a theoretical model underpinning this relationship followed by an empirical application that uses monthly farm, slaughterhouse and retail pork price data for the period 2000–2011. We examine both the relationships of market power with price level transmission and price volatility transmission in the chain. We use a vector error correction model and least squares regressions to analyse price transmission and price volatility transmissions, respectively. Results show that retail market power limited both types of transmissions. Competition inducing policy measures coupled with measures that support price risk management initiatives of chain actors are suggested.