Modeling Spot Markets For Electricity And Pricing Electricity Derivatives

The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives. This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. OrnsteinOCoUhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice. Sample Chapter(s). A Survey of Electricity and Related Markets (331 KB). Contents: A Survey of Electricity and Related Markets; Stochastic Analysis for Independent Increment Processes; Stochastic Models for the Energy Spot Price Dynamics; Pricing of Forwards and Swaps Based on the Spot Price; Applications to the Gas Markets; Modeling Forwards and Swaps Using the HeathOCoJarrowOCoMorton Approach; Constructing Smooth Forward Curves in Electricity Markets; Modeling of the Electricity Futures Market; Pricing and Hedging of Energy Options; Analysis of Temperature Derivatives. Readership: Researchers in energy and commodity markets, and mathematical finance.

Offering a concise but complete survey of the common features of the microstructure of electricity markets, this book describes the state of the art in the different proposed electricity price models for pricing derivatives and in the numerical methods used to price and hedge the most prominent derivatives in electricity markets, namely power plants and swings. The mathematical content of the book has intentionally been made light in order to concentrate on the main subject matter, avoiding fastidious computations. Wherever possible, the models are illustrated by diagrams. The book should allow prospective researchers in the field of electricity derivatives to focus on the actual difficulties associated with the subject. It should also offer a brief but exhaustive overview of the latest techniques used by financial engineers in energy utilities and energy trading desks.

The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives.This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. Ornstein-Uhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice.

The challenges currently facing particIpants m competitive electricity markets are unique and staggering: unprecedented price volatility, a crippling lack of historical market data on which to test new modeling approaches, and a continuously changing regulatory structure. Meeting these challenges will require the knowledge and experience of both the engineering and finance communities. Yet the two communities continue to largely ignore each other. The finance community believes that engineering models are too detailed and complex to be practically applicable in the fast changing market environment. Engineers counter that the finance models are merely statistical regressions, lacking the necessary structure to capture the true dynamic properties of complex power systems. While both views have merit, neither group has by themselves been able to produce effective tools for meeting industry challenges. The goal of this book is to convey the fundamental differences between electricity and other traded commodities, and the impact these differences have on valuation, hedging and operational decisions made by market participants. The optimization problems associated with these decisions are formulated in the context of the market realities of today's power industry, including a lack of liquidity on forward and options markets, limited availability of historical data, and constantly changing regulatory structures.

Finance and energy markets have been an active scientific field for some time, even though the development and applications of sophisticated quantitative methods in these areas are relatively new—and referred to in a broader context as energy finance. Energy finance is often viewed as a branch of mathematical finance, yet this area continues to provide a rich source of issues that are fuelling new and exciting research developments. Based on a special thematic year at the Wolfgang Pauli Institute (WPI) in Vienna, Austria, this edited collection features cutting-edge research from leading scientists in the fields of energy and commodity finance. Topics discussed include modeling and analysis of energy and commodity markets, derivatives hedging and pricing, and optimal investment strategies and modeling of emerging markets, such as power and emissions. The book also confronts the challenges one faces in energy markets from a quantitative point of view, as well as the recent advances in solving these problems using advanced mathematical, statistical and numerical methods. By addressing the emerging area of quantitative energy finance, this volume will serve as a valuable resource for graduate-level students and researchers studying financial mathematics, risk management, or energy finance.

This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.

Master's Thesis from the year 2008 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 6.0, University of Lausanne, 156 entries in the bibliography, language: English, abstract: The Swiss electricity market has been facing structural changes in recent years due to market deregulation activities. This development has been accompanied by the emergence of spot markets where electricity is traded between producer and purchaser. Since the price charged to the end-customer turns out to be more exposed to market prices of electricity, the need for derivatives with a risk management purpose arises. A more recent asset class such as structured products may be used as a risk management tool. This paper focuses on the pricing of various structured products with the Swiss energy price indices as an underlying. Since electricity has particular features that result in a peculiar stochastic process, the pricing of electricity derivatives cannot rely on traditional pricing formulas that have been developed for equity or commodity underlyings. Rather, there is a need for a dynamic model that captures the unique characteristics of electricity. In this paper, a new jump diffusion process is proposed and estimated that is able to incorporate the Swiss electricity price properties. Building on this model, a Monte Carlo simulation is applied that allows one to price differing electricity derivatives that are embedded in structured products. Using the option pricing results, the feasibility and attractiveness of a defined range of structured products is investigated. In order to include the special properties of electricity, new structured products are developed that are more appropriate as risk management tools. One of the main contributions of this paper is the practical approach of how to price structured products. Keywords: Electricity, SWEP, Swissix, Structured Products, Monte Carlo, Jump Diffusion, Derivatives pricing