Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?

Price series that are 21.5 years long for six agricultural futures markets, corn, soybeans, wheat, hogs, coffee and sugar, possess characteristics consistent with nonlinear dynamics. Three nonlinear models, ARCH, long memory and chaos, are able to produce these symptoms. Using daily, weekly and monthly data for the six markets, each of these models is tested against the martingale difference null, one-by-one. Standard ARCH tests suggest that all series might contain ARCH effects, but further diagnostics show that the series are not ARCH processes, failing to reject the null. A long-memory technique, the AFIMA model, fails to find long-memory structures in the data, except for sugar. This allows chaos analysis to be applied directly to the raw data. Carefully specifying phase space, and utilizing correlation dimension and Lyapunov exponent together, the remaining five price series are found to be chaotic processes.futures markets, ARCH, chaos

Economic Dynamics of the German Hog-Price Cycle

We investigated the economic dynamics of the German hog-price cycle with an innovative ‘diagnostic’ modeling approach. Hog-price cycles are conventionally modeled stochastically—most recently as randomly-shifting sinusoidal oscillations. Alternatively, we applied Nonlinear Time Series analysis to empirically reconstruct a deterministic, low-dimensional, and nonlinear attractor from observed hog prices. We next formulated a structural (explanatory) model of the pork industry to synthesize the empirical hog-price attractor. Model simulations demonstrate that low price-elasticity of demand contributes to aperiodic price cycling – a well know result – and further reveal two other important driving factors: investment irreversibility (caused by high specificity of technology), and liquidity-driven investment behavior of German farmers

The Theory of Storage and Price Dynamics of Agricultural Commodity Futures: the Case of Corn and Wheat

Using a restricted version of the BEKK model it is tested an implication of the theory of storage that supply-and-demand fundamentals affect the price dynamics of agricultural commodities. The commodities under analysis are corn and wheat. An interest-storage-adjusted-spread was used as a proxy variable for supply-and-demand fundamentals to test the aforementioned implication for both commodities. It is also tested the Samuelson hypothesis that spot prices have higher volatility than futures prices. It is found that the interest-storage-adjusted-spread has had a statistically significant positive influence on the spot and futures returns for both commodities. Likewise, the results also show that spot price returns have higher volatility compared to futures price returns which is consistent with the Samuelson hypothesis. The results of the aforementioned tests are consistent with both theories and with the existing literature related to commodity futures.Agricultural commodities, BEKK model, multivariate GARCH, Samuelson hypothesis, theory of storage

Explaining the German hog price cycle: A nonlinear dynamics approach

We investigated German hog-price dynamics with an innovative ‘diagnostic’ modeling approach. Hog-price cycles are conventionally modeled stochastically—most recently as randomly-shifting sinusoidal oscillations. Alternatively, we applied nonlinear time series analysis to empirically reconstruct a deterministic, low-dimensional, and nonlinear attractor from observed hog prices. We next formulated a structural (explanatory) model of the pork industry to synthesize the empirical hog-price attractor. Model simulations demonstrate that low price-elasticity of demand contributes to aperiodic price cycling – a well know result – and further reveal two other important driving factors: investment irreversibility (caused by high specificity of technology), and liquidity-driven investment behavior of German farmers

Recurrence analysis techniques for non-stationary and non-linear data

When analysing food consumption data a number of problems arise when one departs from the comparative statics of conventional demand theory. Two of these properties, non-linearity and non-stationarity present a major challenge for econometric modelling. A new method for time series analysis, namely recurrence analysis, is outlined which allows for robust analysis of data that can not be satisfactorily handled with established econometric methods. The method is explained and applied to specific food consumption data. General implications for empirical modelling of similar data are inferred.

Consequences of BSE disease outbreaks in the Canadian beef industry

This study examines farm to wholesale prices spreads to measure the impact of the BSE disease outbreak on the Canadian beef industry. The study uses structure break tests developed by Gregory and Hansen (1996) and Hansen (1992) examine possible breaks within co integrating relationships. The study finds evidence that the industry began realignment as a result of the UK BSE disease outbreak, and the Canadian BSE disease outbreak was simply the largest realignment of the process beginning with the UK disease outbreak. However, the only statistically significant break was the BSE disease outbreak itself in May 2003. Stability was not restored until the border was reopened in 2005. Specific results indicated that the processing sector exploited the border closure in May 2003 to enhance its market power and that the system returned to a competitive one after the border re-opened in July 2005.Beef industry, price transmission, BSE, market power, parameter instability, cointegration with structural break, Agribusiness, Agricultural and Food Policy, Agricultural Finance, GA, IN,

BSE Disease Outbreaks, Structural Change and Market Power in the Canadian Beef Industry

This study examines farm to wholesale prices spreads to measure the impact of the Bovine Spongiform Encephalopathy (BSE) disease outbreak on the Canadian beef industry. The study uses structure break tests developed by Gregory and Hansen (1996) and Hansen (1992) examine possible breaks within cointegrating relationships. The study finds evidence that the industry began a realignment as a result of the UK BSE disease outbreak, and the Canadian BSE disease outbreak was simply the largest realignment of the process beginning with the UK disease outbreak. However, the only statistically significant break was the BSE disease outbreak itself in May 2003. Stability was not restored until the border was reopened in 2005. Specific results indicated that the processing sector exploited the border closure in May 2003 to enhance its market power and that the system returned to a competitive one after the border re-opened in July 2005.BSE, market power, Canada, beef industry, Agribusiness, Industrial Organization, International Relations/Trade,

Chaotic price dynamics of agricultural commodities

Traditionally, commodity prices have been analyzed and modeled in the context of linear generating processes. The purpose of this dissertation is to address the adequacy of this work through examination of the critical assumption of independence in the residual process of linearly specified models. As an alternative, a test procedure is developed and utilized to demonstrate the appropriateness of applying generalized conditional heteroscedastic time series models (GARCH) to agricultural commodity prices. In addition, a distinction is made between testing for independence and testing for chaos in commodity prices. The price series of interest derive from the major international agricultural commodity markets, sampled monthly over the period 1960--1994. The results of the present analysis suggest that for bananas, beef, coffee, soybeans, wool and wheat seasonally adjusted growth rates, ARCH-GARCH models account for some of the non-linear dependence in these commodity price series. As an alternative to the ARCH-GARCH models, several neural network models were estimated and in some cases outperformed the ARCH family of models in terms of forecast ability. This further demonstrated the nonlinearity present in these time series. Although, further examination is needed, all prices were found to be non-linearly dependent. It was determined by use of different statistical measures for testing for deterministic chaos that wheat prices may be an example of such behavior. Therefore, their may be something to be gained in terms of short-run forecast accuracy by using semi-parametric modeling approaches as applied to wheat prices

Testing for nonlinearity and chaos in liquid bulk shipping

ABSTRACT: Modelling and forecasting port traffic are of major importance for the shipping industry. Existence of chaos implies that while long term forecasting is vain, reliable short-term forecasting would be possible. The objective of this research is to uncover the nonlinear dynamics and chaotic behavior of the liquid bulk cargo shipping, using monthly data from January 1992 to March 2013 for the Spanish seaports. For this purpose, in first instance we remove any linear dependence by means of the Box-Jenkins approach. Afterwards we analyzed the existence of nonlinearity and chaotic behavior by applying the BDS and the Lyapunov test respectively. Our findings suggest that although there has been found a dominant nonlinear structure underlying the dynamics of the liquid bulk traffic, determinism cannot be assumed and hence chaos cannot be inferred. These results are especially relevant for modeling and forecasting of maritime traffic, specifically for liquid bulk cargo, and for the design and evaluation of public policies related to the investment planning and management of port system