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.

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.

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.

Fluctuations of commodity prices, most notably of oil, capture considerable attention and have been tied to important economic effects. This book advances our understanding of the consequences of these fluctuations, providing both general analysis and a particular focus on the countries of the Pacific Rim.

Deals with the theory and methods required for specifying, estimating, validating, and applying commodity models which describe behavior of a quarterly or annual nature, though certain ...

This dissertation addresses the gap between commodity price models in economics and finance. The literature in finance often abstracts from market forces and calibrates a stochastic process of price dynamics in order to follow them closely and to price commodity derivatives, most importantly futures contracts. On the other hand models in economics literature often focus on supply, demand and inventories in the long-run. I have developed short-run structural models of commodity prices. These models provide a better description of price dynamics by considering the underlying structure of the economy. Since these models incorporate actions of market participants, they have the advantage of being able to process information signals about probabilities of future supply/demand shocks. The other advantage of short-run structural models is their power in prediction of unobservable states of the economy. Hence, these models provide a better description of forward curves in commodities markets. Recent advances in the theory of storage have been able to associate specific behaviors of commodity prices with inventory dynamics. These models assume producers and consumers who only consider current price, and storage units who consider the whole stochastic process of price in the future. This thesis improves upon these models in two aspects. First, I remove the assumption that the producers and consumers take into account only the current price. For depletable commodities specifically, and for many commodities in general, it is more plausible to assume that the producer has the option to sell the commodity now or postpone the extraction until a future time. The expected future dynamics of prices can change the current production decisions and as a result the current and future prices. My model characterizes the equilibrium of such a system and its comparative dynamics. Second, I introduce an advanced calibration algorithm for this model. Traditional models calibrate their parameters by minimizing their prediction error on aggregate measures such as the average volatility of forward prices. My approach considers the instances of forwards curves and tries to matches each of them. One advantage of this model is the ability to estimate the state of the system (e.g. remaining inventories) as well as the transient and permanent shocks in supply/demand. The theoretical framework of this dissertation shows that actions of rational market participants impose certain price dynamics to the market. Most examples in this work consider crude oil as it is the most traded commodity, with liquid future contracts for longer horizons. Calibration results demonstrate the improvements that short-run structural models could create in describing price dynamics.

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 aluminum to oil to soybeans to zinc.