Modeling And Estimation Of Commodity Price Dynamics
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Modeling and Estimation of Commodity Price Dynamics
Commodity prices exhibit different characteristics than traditional asset classes. This paper provides an in-depth analysis of the corresponding price dynamics transferring a time series approach originally proposed by Chan et al. (1992) to the field of commodities. One unrestricted and eight restricted stochastic models are assessed and empirically tested. Besides an incorporated mean reversion feature, the model also allows the volatility to change with the underlying price. Daily data of 22 commodities out of different sectors are taken into consideration. Generic front month future contracts form July 24, 1997 to January 6, 2011 were used for the analysis (3511 observations), further splitting the time series into a bull (2002 - 2006) and bear market regime (2007 - 2008). Generalized Method of Moments (GMM) are applied to estimate the unknown parameters and a &u9672 goodness-of-fit test is run to evaluate which models capture best the dynamics of the corresponding commodities for different market regimes. In line with Geman and Shih (2009) we find that the CEV exponent &u947 plays a very important role in the modeling of commodity price dynamics whereas the mean reversion effect disappears for most of the commodities for the different periods under analysis.
Modeling and Forecasting Primary Commodity Prices
Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.
Commodity Price Dynamics
Author: Craig Pirrong
language: en
Publisher: Cambridge University Press
Release Date: 2011-10-31
Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.