Are commodity prices chaotic?

Energy Economics, 2018

Energy commodity prices have been examined over the last 20 years to detect the presence of chaos as an alternative to stochastic models, with contrasting results. In this paper, we wish to reassess the chaotic paradigm in the light of two new pieces of information with respect to the literature: the appearance of noise-aware estimation methods for the correlation entropy and the availability of longer time series. Our analysis shows that the literature has heavily underestimated the presence of noise, and that chaotic characteristics coexist with stochastic ones in the time series of prices. Through the recurrence quantification analysis, we have also observed the presence of intermittency, where periods of regular behaviour are replaced by periods of chaotic behaviour, which could explain the emergence of bubbles.

Uncertain movement in commodity prices is a major concern for policy makers. Generalised autoregressive conditional heteroscedasticity (GARCH) model was applied to measure the extent of volatility in spot prices due to futures trading. The study sourced the available daily spot prices of selected twenty agricultural commodities that are traded in NCDEX platform both for 2009-10 (period of peak inflation) and right from the date of commencement of trading till June 2014. Empirical results indicated low price volatility in maize, soybean, cotton seed oilcake, castor, palm oil, cumin and chilli during the peak inflation period i.e., 2009-10; whereas, chickpea, cotton seed oilcake, mustard and cumin experienced the same level of volatility right from inception of trading. The present study concludes that futures market helps to reduce price volatility but not necessarily in all the commodities. Hence, it is recommended that the commodity exchanges should continue the trading in commodit...

Resources Policy, 2018

The possible scarcity of copper (and the likely resulting pressure on prices) is an issue of concern, especially in the light of its importance for the ever growing networking industry. Also for that reason, copper is the nonferrous metal most traded in the markets. Therefore, assessing the nature of its price fluctuations is an important task. Several papers have been devoted to analysing the characteristics of the time series of copper prices, especially for the purpose of predicting its future behaviour. The field of approaches can be divided roughly equally between those adopting a stochastic model and those opting for a deterministic nonlinear (chaotic) model. Nevertheless, while papers employing the stochastic paradigm have completely ignored the presence of chaotic features, at the same time papers recognizing the chaotic paradigm have neglected the presence of noise.The purpose of this paper is to investigate copper price behaviour in the CMX, considering a very long time series and adopting estimation methods that provide the coexistence of stochastic and chaotic features. We find that: a) the presence of noise is very significant (amounting to more than a quarter of the average signal value), as well as the presence of chaotic features; b) intermittency is present, which may be indicative of a bubblerelated value that emerged without any fundamental cause.

Agricultural Systems, 1997

arXiv: Statistical Finance, 2017

We test whether the futures prices of some commodity and energy markets are determined by stochastic rules or exhibit nonlinear deterministic endogenous fluctuations. As for the methodologies, we use the maximal Lyapunov exponents (MLE) and a determinism test, both based on the reconstruction of the phase space. In particular, employing a recent methodology, we estimate a coefficient $\kappa$ that describes the determinism rate of the analyzed time series. We find that the underlying system for futures prices shows a reliability level $\kappa$ near to $1$ while the MLE is positive for all commodity futures series. Thus, the empirical evidence suggests that commodity and energy futures prices are the measured footprint of a nonlinear deterministic, rather than a stochastic, system.

2016

This paper contains a set of tests for nonlinearities in energy commodity prices. The tests comprise both standart diagnostic tests for revealing nonlinearities. The latter test procedures make use of models in chaos theory, so-called long-memory models and some asymmetric adjustment models. Empirical tests are carried our with daily data for crude oil, heating oil, gasoline and natural gas time series covering the period 2010-2015. Test result showed that there are strong nonlinearities in the data. The test for chaos, however, is weak or nonexisting. The evidence on long memory (in terms of rescaled range and fractional differencing) is somewhat stronger altough not very compelling.

We introduce a multi-factor stochastic volatility model based on the CIR/Heston volatility process that incorporates seasonality and the Samuelson effect. First, we give conditions on the seasonal term under which the corresponding volatility factor is well-defined. These conditions appear to be rather mild. Second, we calculate the joint characteristic function of two futures prices for different maturities in the proposed model. This characteristic function is analytic. Finally, we provide numerical illustrations in terms of implied volatility and correlation produced by the proposed model with five different specifications of the seasonality pattern. The model is found to be able to produce volatility smiles at the same time as a volatility term-structure that exhibits the Samuelson effect with a seasonal component. Correlation, instantaneous or implied from calendar spread option prices via a Gaussian copula, is also found to be seasonal.

Journal of Economic Surveys, 1988

The Manchester School, 1999

This paper presents and implements a number of tests for non-linear dependence and a test for chaos using transactions prices on three LIFFE futures contracts: the Short Sterling interest rate contract, the Long Gilt government bond contract, and the FTSE-100 stock index futures contract. While previous studies of high frequency futures market data use only those transactions which involve a price change, we use all of the transaction prices on these contracts whether they involve a price change or not. Our results indicate irrefutable evidence of non-linearity in two of the three contracts, although we find no evidence of a chaotic process in any of the series. We are also able to provide some indications of the effect of the duration of the trading day on the degree of non-linearity of the underlying contract. The trading day for the Long Gilt contract was extended in August 1994, and prior to this date there is evidence of only a linear structure in the return series. However, after the extension of the trading day we do find evidence of a non-linear return structure.

arXiv: Statistical Finance, 2016

In this paper we study the possible "chaotic" nature of some energy and commodity futures time series (like heating oil and natural gas, among the others). In particular the sensitive dependence on initial conditions (the so called "butterfly effect", which represents one of the characterizing properties of a chaotic system) is investigated estimating the Kolmogorov entropy, in addition to the maximum Lyapunov exponent. The results obtained with these two methods are consistent and should indicate the presence of butterfly effect. Nevertheless, this phenomenon - which is usually showed by deterministic systems - is not here completely deterministic. In fact, using a test introduced by Kaplan and Glass, we prove that, for all the series analyzed here, the stochastic component and the deterministic one turn up to be approximately in the same proportions. The presence of butterfly effect in energy futures markets is a controversial matter, and the evaluations obtain...

Abstract The aim of this study is to examine the existence of chaotic structure in agricultural production in Turkey by using “Chaotic Dynamic Analysis (CDA)” and to provide accurate forecast of agricultural production. The data of wheat, barley and rice production in Turkey was obtained from Turkish Statistical Institute (TURKSTAT) and covers the period of 1991 to 2009. Our analysis shows that the supply of the selected agricultural products has chaotic structure.

SSRN Electronic Journal, 2000

This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of , FIEGACH model of , and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.

2015

This decade has seen movements in commodity futures markets never seen before. There are many factors that have intensified price movements and volatility behavior. Those factors likely altering supply and demand include governmental policy within and outside of the U.S, weather shocks, geopolitical conflicts, food safety concerns etc. Whatever the reasons are for price movements it is clear that the volatility behavior in commodity markets constantly change, and risk managers need to use current and efficient tools to mitigate price risk. This study identified market structural breaks of realized volatility in corn, wheat, soybeans, live cattle, feeder cattle and lean hogs futures markets. Furthermore, this study analyzes the forecasting performance of implied volatility, historical volatility, a composite approach and a naïve approach as forecasters of realized volatility. The forecasting performance of these methods was analyzed in the full period of time of our weekly data from January 1995 to April 2014 and in each identified market regime for each commodity. Previous research has

Finance and Market

This study investigates the chaos effect of agricultural exchange-traded funds (ETFs) using Brock, Dechert, and Scheinkman test, rescaled range analysis, and correlation dimension analysis. The standardized residuals from generalized autoregressive conditional heteroskedasticity models are fitted into eight ETFs and examined in each case for evidence of chaotic behavior. This study also examines whether or not the ETFs are consistent with the chaos effect based on the underlying random data with trend-reinforcing series. Research results outline the financial insights for the agricultural ETF field of investment forecasting to eliminate trading emotions, while pursuing considerable profitable experience for investors.

Financial Innovation, 2017

Background: This paper examines the pattern of the volatility of the daily return of select commodity futures in India and explores the extent to which the select commodity futures satisfy the Samuelson hypothesis. Methods: One commodity future from each group of futures is chosen for the analysis. The select commodities are potato, gold, crude oil, and mentha oil. The data are collected from MCX India over the period 2004-2012. This study uses several econometric techniques for the analysis. The GARCH model is introduced for examining the volatility of commodity futures. One of the key contributions of the paper is the use of the β term of the GARCH model to address the Samuelson hypothesis. Result: The Samuelson hypothesis, when tested by daily returns and using standard deviation as a crude measure of volatility, is supported for gold futures only, as per the value of β (the GARCH effect). The values of the rolling standard deviation, used as a measure of the trend in the volatility of daily returns, exhibits a decreasing volatility trend for potato futures and an increasing volatility trend for gold futures in all contract cycles. The result of the GARCH (1,1) model suggests the presence of persistent volatility and the prevalence of long memory for the select commodity futures, except potato futures. Conclusions: The study sheds light on significant characteristics of the daily return volatility of the commodity futures under analysis. The results suggest the existence of a developed market for the gold and crude oil futures (with volatility clustering) and show that the maturity effect is only valid for the gold futures.