Modeling Energy Demand using Nonparametric and Extreme Value Theory

Modeling demand and price data using nonparametric methods and extreme value theory provides an up-to-date picture on how extreme events can be modeled. In this book, kernel smoothing based conditional quantile approach, a nonparametric procedure is used to model volatile demand data. Nevertheless, quantile regression procedures work well in non extreme parts of a given data but poorly on extreme levels. This book applies the threshold model of extreme value in order to circumvent the lack of observation problem at the tail of the distribution. Various kernel estimation methods and extreme value theory are discussed and the asymptotic properties of the estimators given. The methods are applied to model extremes in electricity demand and fuel price data. A combination of nonparametric approach and extreme value theory is used as an estimation of value at risk. Value at risk is chosen in this book as it is extensively used in practice.This book will be a valuable reference for research in applied statistics, actuarial science and management science and will serve as a text book for graduate students and others who are interested in modeling of extreme events.

Levi Mbugua, Ph.D, has taught Statistics in Kenyan universities for several years.He holds a Ph.D. degree in Applied Statistics from Jomo Kenyatta University of Agriculture and Technology and M.Sc. degree from The University of Nairobi. Currently he is the chair of the Department of Mathematics and Physical Sciences at Maasai Mara University,Kenya