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.

[1]  Anastasios Malliaris,et al.  Methodological issues in asset pricing: Random walk or chaotic dynamics , 1999 .

[2]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[3]  H. Lorenz.  Nonlinear Dynamical Economics and Chaotic Motion , 1989 .

[4]  Walter C. Labys,et al.  The existence of metal price cycles , 1998 .

[5]  G Sugihara,et al.  Nonlinear control of heart rate variability in human infants. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[6]  John Moody,et al.  IMPROVED ESTIMATES FOR The rescaled range and Hurst exponents , 1995 .

[7]  W. Arthur.  Inductive Reasoning and Bounded Rationality , 1994 .

[8]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[9]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[10]  Michel Terraza,et al.  Stochastic chaos or ARCH effects in stock series?: A comparative study , 2002 .

[11]  Laura Gardini,et al.  Speculative Behaviour and Complex Asset Price Dynamics , 2001 .

[12]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[13]  Walter C. Labys,et al.  Evidence of chaos in commodity futures prices , 1992 .

[14]  Jiang Wang,et al.  A Model of Competitive Stock Trading Volume , 1994, Journal of Political Economy.

[15]  Jiang Wang,et al.  A Model of Intertemporal Asset Prices Under Asymmetric Information , 2011 .

[16]  H. White.  Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models , 1989 .

[17]  Christian Jost,et al.  Heterogeneous real-time trading strategies in the foreign exchange market , 1995 .

[18]  T. Lux.  The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions , 1998 .

[19]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[20]  David Ruelle,et al.  Deterministic chaos: the science and the fiction , 1995 .

[21]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[22]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[23]  K. Nishimura.  Expectation heterogeneity and price sensitivity , 1998 .

[24]  E. Phelps,et al.  Individual Forecasting and Aggregate Outcomes: 'Rational Expectations' Examined , 1987 .

[25]  William A. Barnett,et al.  A single-blind controlled competition among tests for nonlinearity and chaos , 1997 .

[26]  Catherine Kyrtsou,et al.  Evidence for Neglected linearity in Noisy Chaotic Models , 2005, Int. J. Bifurc. Chaos.

[27]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[28]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[29]  Laura Gardini,et al.  Speculative behaviour and complex asset price dynamics: a global analysis , 2002 .

[30]  J. R. Wallis,et al.  Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence , 1969 .

[31]  R. Davidson,et al.  Walvelet Analysis of Commodity Price Behavior , 1998 .

[32]  Amos Golan,et al.  Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets , 1999 .

[33]  W. Brock,et al.  Heterogeneous beliefs and routes to chaos in a simple asset pricing model , 1998 .

[34]  Ramazan Gençay,et al.  Nonlinear modelling and prediction with feedforward and recurrent networks , 1997 .

[35]  A. Lo.  Long-Term Memory in Stock Market Prices , 1989 .

[36]  B. Raymond.  Polynomially Bounded Rationality , 1994 .

[37]  H. Kantz.  A robust method to estimate the maximal Lyapunov exponent of a time series , 1994 .

[38]  Walter C. Labys,et al.  Fractional dynamics in international commodity prices , 1997 .

[39]  S. Sosvilla‐Rivero,et al.  Testing nonlinear forecastability in time series: Theory and evidence from the EMS , 1998 .

[40]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[41]  Clive W. J. Granger,et al.  Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests , 1993 .

[42]  Kenneth A. Froot,et al.  Chartists, Fundamentalists and the Demand for Dollars , 1991 .

[43]  T. Lux.  Herd Behaviour, Bubbles and Crashes , 1995 .

[44]  Andrea Gaunersdorfer,et al.  Endogenous fluctuations in a simple asset pricing model with heterogeneous agents , 2000 .