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Modeling and Forecasting Primary Commodity Prices
Inglês
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The publication of this book at this time is particularly important. 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 utilise 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.
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