Nonparametric Density Estimation for Energy Price Returns

Abstract

Sitting on the very tip of my chair, feigning interest in the mumbled string of motivational buzz words spouting out of the mouth of an unusually dull director of global risk, it occurred to me that if I looked hard enough, through the gray mist of the incoherent mutterings, there would emerge some shape, some form to their ideas, which as yet my colleagues and I could not perceive. I mused on this thought, toyed with the idea of developing a statistical algorithm that would filter out the noise, revealing the underlying structure. My jocose thoughts were shattered by what was supposed to be the motivational crescendo — we all rose to our feet and clapped our hands somewhat like well-fed seals at feeding time at the local zoo — that is, with not much enthusiasm. Unfortunately, for that individual, there was no form to his ideas, no shape to his plan. Needless to say the listless, MBA-clad, mumbo-jumbo speaking “hot shot” was not head of global risk for very long. However, the experience stuck in my mind and re-stimulated my interest in nonparametric statistical methods, a subset of which, non parametric density estimation, is the subject of this chapter. It introduces nonparametric density estimation as a complementary statistical mechanism for describing energy price returns. It begins by discussing, the simplest nonparametric density estimator — the histogram, how to construct it, and its properties and limitations.KeywordsKernel DensityPrice ReturnKernel Density EstimatorNonparametric Density EstimationBandwidth SelectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.