By Craig Pirrong
Commodities became an incredible section of many traders' portfolios and the focal point of a lot political controversy over the last decade. This e-book makes use of structural types to supply a greater knowing of the way commodities' costs behave and what drives them. It exploits transformations 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 necessary to students, investors, and coverage makers who are looking to greater comprehend frequently perplexing - and severe - hobbies within the costs of commodities from aluminum to grease to soybeans to zinc. [C:\Users\Microsoft\Documents\Calibre Library]
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Extra info for Commodity Price Dynamics: A Structural Approach
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.
8) subject to the initial boundary condition F (y, z, x, 0) = P (y, z, x), where I have expanded the notation to include a fourth argument in the forward price function to represent time to expiration. 14 The PDE approach is attractive because there are robust, well-known methods to solve such equations. Specifically, finite difference methods are widely employed in finance, engineering, and the physical sciences to solve PDEs. These methods have been the subject of intense study and, as a result, it is possible to draw on a vast body of work and greatly refined and experience-tested techniques.
This increases computational costs substantially and requires the development of additional techniques. The most widely employed method for solving 2D PDEs is called the alternating direction implicit method, or ADI. Although widely employed, it has some deficiencies that arise from (a) its reliance on explicit finite differences as well as implicit ones, and (b) its inability to handle problems where the y and z variables have a non-zero correlation. There is another and, in my view, highly superior way to solve 2D PDEs.