By Daniel Mahoney
Commodity markets current numerous demanding situations for quantitative modeling. those contain excessive volatilities, small pattern info units, and actual, operational complexity. additionally, the set of traded items in commodity markets is extra constrained than in monetary or fairness markets, making worth extraction via buying and selling tougher. those evidence make it really easy for modeling efforts to run into critical difficulties, as many types are very delicate to noise and consequently can simply fail in perform.
Modeling and Valuation of power buildings is a complete consultant to quantitative and statistical techniques which were effectively hired in help of buying and selling operations, reflecting the author's 17 years of expertise as a front-office 'quant'. the foremost subject matter of the publication is that easier is mostly greater, a message that's drawn out during the truth of incomplete markets, small samples, and informational constraints. the required mathematical instruments for figuring out those matters are completely constructed, with many innovations (analytical, econometric, and numerical) amassed in one quantity for the 1st time. a selected emphasis is put on the vital position that the underlying marketplace answer performs in valuation. Examples are supplied to demonstrate that powerful, approximate valuations are to be most well liked to overly formidable makes an attempt at distinct qualitative modeling.
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Additional resources for Modeling and Valuation of Energy Structures: Analytics, Econometrics, and Numerics
Indeed, there is no doubt of the existence of the so-called Samuelson effect in commodity markets, namely the term structure of volatility. While this effect is commonly 16 Modeling and Valuation of Energy Structures associated with mean reversion of the underlying spot process,9 this does not necessarily have to be the driving force. We will return to these points in great detail later, but they should be kept in mind as we discuss conventional techniques that rely on a rather strict delineation between stationary and non-stationary processes.
What is going on of course is that the estimator is converging at a rate faster than O( √1 ), in fact with rate O( T1 ). 22) where w is a standard Brownian motion. 22) has a nonstandard distribution and the critical values (for acceptance/rejection of inferences) are typically obtained via simulation. The point we are trying to make here is that, in the presence of non-stationary, the relevant diagnostics can radically change, even if the underlying estimation algorithm is unchanged. This point is worth emphasizing.
To the extent that (at least some of) these fundamental factors may be relatively well understood (in comparison to commodities themselves), such information should be exploited as much as possible. In particular, we should endeavor to understand how the time scales inﬂuencing these factors manifest themselves in the dynamics of energy prices and, if possible, separated out from the analysis of these dynamics. 3. , supply shocks). For our purposes we can think of the heat rate as equivalent to temperature or load/demand.
Modeling and Valuation of Energy Structures: Analytics, Econometrics, and Numerics by Daniel Mahoney