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Commodity Price Dynamics: A Structural Approach by Craig Pirrong

By Craig Pirrong

Commodities became a big portion of many traders' portfolios and the focal point of a lot political controversy over the last decade. This booklet makes use of structural types to supply a greater realizing of ways commodities' costs behave and what drives them. It exploits variations throughout commodities and examines quite a few predictions of the versions to spot the place they paintings and the place they fail. The findings of the research are invaluable to students, investors, and coverage makers who are looking to larger comprehend frequently confusing - and severe - routine within the costs of commodities from aluminum to grease to soybeans to zinc. [C:\Users\Microsoft\Documents\Calibre Library]

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16 If μz and σz depend on z, these coefficients also depend on the grid location; that is, on the i . 12 on Tue Oct 09 07:55:36 BST 2012. 3 A More Detailed Look at the Numerical Implementation 39 We now have Nz equations in Nz unknowns. Nz − 2 of the equations come from the PDE and the finite difference approximations. The remaining two come from the boundary conditions. It is straightforward to solve these Nz equations. Once this is done, one has solved for the forward price as of δt years prior to expiration for each xˆ in the carry-in grid and each z in its grid.

To follow the road map, it is necessary to make more specific assumptions about functional forms and, perhaps more important, about the behavior of the demand and supply shocks: that is, about the relevant pricing measure. In so doing, it is first necessary to acknowledge that we operate under the curse of dimensionality. Thus, although heretofore I have proceeded as if there is an arbitrary number of random shocks in the economy, practical computational considerations – the curse of dimensionality – effectively make it necessary to limit the analysis to one or two.

Deaton and Laroque (1995, 1996) allow for autocorrelated demand shocks (again in a one-factor model). Based on an analysis of annual data, they find that virtually all the autocorrelation in commodity prices is attributable to autocorrelation in the underlying demand disturbances, and very little is attributable to the smoothing effects of speculative storage. The Deaton-Laroque empirical analyses are problematic for several reasons. First, they utilize low-frequency (annual) data for a wide variety of very heterogeneous commodities.

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