The book is divided into four chapters. The first one introduces the structure of deregulated, competitive electricity markets with the power pools and power exchanges as the basic marketplaces for price discovery. Chapter 2 reviews the so-called stylized facts of selected power markets. In...

The book can be classified as a work from a new quickly developing domain of applied and computational mathematics called numerical probability or computational stochastics. Authors present new computer methods of construction and investigation of various stochastic models describing problems...

This monograph is based on methods and numerical tools from such fields as theory of stochastic differential equations (SDEs), stochastic modeling in computational physics, engineering and mathematical finance, statistical estimation methods, and Monte-Carlo type approximations.

Statistical Tools for Finance and Insurance presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance. Written by practitioners and leading academics in the field, this book offers a unique combination of...

Statistical Tools for Finance and Insurance presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance. Written by practitioners and leading academics in the field this book offers a unique combination of...

We show that incorporating the intra-day and inter-zone relationships of electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) Interconnection improves the accuracy of short- and medium-term forecasts of average daily prices for a major PJM market hub -- the Dominion Hub in...

This paper proposes an agent-based modeling (ABM) approach to study the diffusion and adoption of dynamic electricity tariffs. We discuss the difference between opinions and decisions of electricity consumers regarding dynamic pricing. By means of a simple ABM, we provide a plausible explanation...

Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead...

We examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to...

Probabilistic load forecasting is becoming crucial in today's power systems planning and operations. We propose a novel methodology to compute interval forecasts of electricity demand, which applies a Quantile Regression Averaging (QRA) technique to a set of independent expert point forecasts....