Food Price Volatility and Its Implications for Food Security and Policy


Book Description

This book provides fresh insights into concepts, methods and new research findings on the causes of excessive food price volatility. It also discusses the implications for food security and policy responses to mitigate excessive volatility. The approaches applied by the contributors range from on-the-ground surveys, to panel econometrics and innovative high-frequency time series analysis as well as computational economics methods. It offers policy analysts and decision-makers guidance on dealing with extreme volatility.




What Explains the Rise in Food Price Volatility?


Book Description

The macroeconomic effects of large food price swings can be broad and far-reaching, including the balance of payments of importers and exporters, budgets, inflation, and poverty. For market participants and policymakers, managing low frequency volatility—i.e., the component of volatility that persists for longer than one harvest year—may be more challenging as uncertainty regarding its persistence is likely to be higher. This paper measures the low frequency volatility of food commodity spot prices using the spline- GARCH approach. It finds that low frequency volatility is positively correlated across different commodities, suggesting an important role for common factors. It also identifies a number of determinants of low frequency volatility, two of which—the variation in U.S. inflation and the U.S. dollar exchange rate—explain a relatively large part of the rise in volatility since the mid-1990s.




Do Volatility Determinants Vary across Futures Contracts? Insights from a Smoothed Bayesian Estimator


Book Description

We apply a new Bayesian approach to multiple-contract futures data. It allows the volatility of futures prices to depend upon physical inventories and the contract's time to delivery - and it allows those parametric effects to vary over time. We find a time-varying negative relationship between lumber inventories and lumber futures price volatility. The Bayesian approach leads to different conclusions regarding the size of the inventory effect than does the standard method of parametric restrictions across contracts. The inventory effect is smaller for the most recent contracts, possibly due to increasing inventories over time. In contrast, the Bayesian approach does not lead to substantively different conclusions about the time-to-delivery effect than do traditional frequentist methods.







Volatility and Commodity Price Dynamics


Book Description

Commodity prices tend to be volatile, and volatility itself varies over time. changes in volatility can affect market variables by directly affecting the marginal value of storage, and by affecting a component of the total marginal cost of productions: the opportunity cost of exercising the option to produce the commodity now rather than waiting for more price information. I examine the role of volatility in short-run commodity market dynamics, as well as the determinants of volatility itself. Specifically, I develop a model describing the joint dynamics of inventories, spot and futures prices, and volatility, and estimate it using daily and weekly data for the petroleum complex: crude oil, heating oil, and gasoline.




The Economics of Food Price Volatility


Book Description

"The conference was organized by the three editors of this book and took place on August 15-16, 2012 in Seattle."--Preface.




Trading Mechanisms, Speculative Behavior of Investors, and the Volatility of Prices


Book Description

This paper compares the volatility of spot prices (dealership market) with that of futures prices (auction market) to test the implications of different trading mechanisms for the volatility of prices. First, a natural estimator of the volatility is sued. Using the intraday data of the major Market Index and its futures prices, we show that the volatility of opening prices is higher than that of closing prices not only in the spot market but in the futures market, and that the intraday volatility patterns are U-shaped in both markets. Of particular interest is that futures prices do not appear to be as volatile as spot prices when the natural estimator of volatility is used, to the contrary of the conventional wisdom. We argue that the different volatility patterns during the day are not necessarily due to the different trading mechanisms, auction market versus dealership market. Instead, after developing a simple theoretical model of speculative prices, we show that at least part of the different volatility patterns during the day may be attributable to speculative behavior of investors based on heterogeneous information. In addition, we further investigate the volatilities of spot and futures prices using a temporal estimator of price volatility as an alternative to the natural estimator. Based on the temporal estimator, we cannot find any systematic pattern of volatilities during the day in both spot and futures markets, and that futures prices appear to be more volatile than spot prices in terms of how quickly the price moves beyond a given unit price level, but not in terms of how much the price changes during a given unit time interval. Some policy implications are also discussed.