Noisy chaotic dynamics in commodity markets

Abstract

The nonlinear testing and modeling of economic and financial time series has increased substantially in recent years, enabling us to better understand market and price behavior, risk and the formation of expectations. Such tests have also been applied to commodity market behavior, providing evidence of heteroskedasticity, chaos, long memory, cyclicity, etc. The present evaluation of futures price behavior confirms that the resulting price movements can be random, suggesting noisy chaotic behavior. Prices could thus follow a mean process that is dynamic chaotic, coupled with a variance that follows a GARCH process. Our conclusion is that models of this type could be constructed to assist in forecasting prices in the short run but not over long run time periods. Copyright Springer-Verlag 2004Commodity futures prices, risk and price expectations, noisy chaotic processes, correlation dimension analysis, nonlinear models, short term price forecasting, C22, E31, E32,

A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

“…GARCH model performed better in stimulating time series with long-term memory. Krytsou et al [18] proposed that long-term forecasting of noisy chaotic return series no longer worked. Instead, Mackey-Glass-GARCH model could be used.…”

Complexity, Chaos, and the Duffing-Oscillator Model: An Analysis of Inventory Fluctuations in Markets

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