Modeling And Forecasting Primary Commodity Prices

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 reflect 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 recent modeling approaches including STS, ARIMA, ARFIMA, and ARCH and GARCH models. Practical applications focus on the price behavior of more than twenty international commodity markets. The publication of this book at this time is particularly important. Recent economic growth in China and other Asian countries has led commodity demands and has caused price increases and accompanying price fluctuations not only for crude oil but also for the many other commodity raw materials. World commodity markets are again under scrutiny and the modeling and forecasting of these markets is as important now as ever before.