Power, Gabriel. 2007. A Wavelet-Based Analysis of Commodity Futures Markets. Doctoral
Dissertation, Cornell University.
The time horizon of
decision-making is an essential dimension of economic problems but is difficult
to explicitly define. In this thesis, we use time series analysis augmented by
wavelet transform methods to precisely identify distinct time horizons in
economic data and measure their explanatory power. This enables us to address
three timely and persistent questions in the literature on commodity
derivatives markets are addressed. First, are findings of long memory
(fractional integration) in commodity futures price volatility spurious, following
Granger?s conjecture? Yes, only two out of eleven commodities are characterized
by true long memory and certain stochastic break models (e.g. Markov-switching)
are found to be more plausible. Second, do large Index Traders such as
commodity pools and pension funds increase futures price volatility through a
large volume of trading activity? This appears to be true only for non-storable
commodity contracts. Third, can we improve the accuracy of term structure
models of futures prices by (i) including more state variables to better
capture maturity and inventory effects, and (ii) filtering out what appears to
be noise at the shortest time horizons? The results suggest that (i) three
state variables is an optimal choice and (ii) estimates using filtered data are
not improved and the noise may be economically meaningful.
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