Chaos in oil prices? Evidence from futures markets

Abstract

We test for the presence of low-dimensional chaotic structure in crude oil, heating oil, and unleaded gasoline futures prices from the early 1980s. Evidence on chaos will have important implications for regulators and short-term trading strategies. While we find strong evidence of non-linear dependencies, the evidence is not consistent with chaos. Our test results indicate that ARCH-type processes, with controls for seasonal variation in prices, generally explain the non-linearities in the data. We also demonstrate that employing seasonally adjusted price series contributes to obtaining robust results via the existing tests for chaotic structure. Maximum likelihood methodologies, that are robust to the non-linear dynamics, lend support for Samuelson's hypothesis on contract-maturity effects in futures price-changes. However, the tests for chaos are not found to be sensitive to the maturity effects in the futures contracts. The results are robust to controls for the oil shocks of 1986 and 1991.

Crises, crude oil and BRIC stock markets

“…Complementarily, a large body of literature suggests that crude oil prices have a statistically significant effect on economic activity (Adrangi, et al, 2001;Berument, et al, 2010;Brown & Yücel, 2001;Costantini & Martini, 2010;Fofana, et al, 2009;Hamilton, 2009aHamilton, , 2009bHanabusa, 2009;Hsing, 2007;Huang, et al, 1996;Jayaraman & Choong, 2009;Jiao & Ma, 2006;Jones, et al, 2004b;Odusami, 2010;Oladosu, 2009;Papapetrou, 2001;Rafiq, et al, 2009;Reynolds & Kolodziej, 2007;Zagaglia, 2010). Uri (1996) indicated the effect of changes in the price of crude oil on agricultural employment in the USA between 1947 and 1995 using Granger Causality.…”

Crude Oil Market Pricing Mechanism analysis and Simulation

“…Fig.2 shows elements of function (1). If max Lyapunov exponent of a data series is more than zero, then the data series has chaos characteristics.…”

“…According to chaos theory and fractal theory, there is a relationship between history price series. To analyze this relationship, some reports had researched chaos mechanism of crude oil market [1], and others had try to model the mechanism of crude oil market [2] [3] [4].Forecasting is important purpose of those studies. Comparing to research by chaos theory [5] [6] [7] [8], some researches which modeling and forecasting crude oil market [9] [10], had utilized regression model, such as ARCH-type models [11].…”

Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

“…Further, Alvarez et al (2008) showed in their research that the random walk-type behavior in energy futures prices, thus the autocorrelation in oil prices diminishes over time. Adrangi et al (2001) tested the presence of low-dimensional chaotic structure in crude oil, heating oil, and unleaded gasoline futures prices with their sample starting by the early 1980s. In their study, they pointed at chaotic structure and high persistence in the series investigated and concluded that chaotic structure and high persistence in the data would create strong implications for regulators and short-term trading strategies.…”