A Wavelet-Based Analysis of Commodity Futures Markets

The time horizon of decision-making is an essential dimension of economic problemsbut is difficult to explicitly define. In this thesis, we use time series analysisaugmented by wavelet transform methods to precisely identify distinct time horizonsin economic data and measure their explanatory power. This enables us to addressthree timely and persistent questions in the literature on commodity derivativesmarkets are addressed. First, are findings of long memory (fractional integration) incommodity futures price volatility spurious, following Granger?s conjecture? Yes,only two out of eleven commodities are characterized by true long memory and certainstochastic break models (e.g. Markov-switching) are found to be more plausible.Second, do large Index Traders such as commodity pools and pension funds increasefutures price volatility through a large volume of trading activity? This appears to betrue only for non-storable commodity contracts. Third, can we improve the accuracyof term structure models of futures prices by (i) including more state variables tobetter capture maturity and inventory effects, and (ii) filtering out what appears to benoise at the shortest time horizons? The results suggest that (i) three state variables isan optimal choice and (ii) estimates using filtered data are not improved and the noisemay be economically meaningful.