The nature of energy markets is such that often complex models are used to assist in decision-making. Time-series models, regime switching, long-term short-term are examples of many dazzling names. For many, these models and techniques are a black box on which many decisions are based. This book aims to open this black box and its goal is to provide the reasoning behind various techniques and models that are frequently used, and to indicate applications that they are used for.
The current credit crisis is forcing large energy consumers (big companies) to be more aware of the credit risk they face from energy distribution companies, and they are looking for alternatives. This book will be written from a practical angle.
This book combines both academic and practical approaches.
It is intended for risk and portfolio managers, analysts and researchers that work for energy companies, banks and energy investment companies, students, academic researchers and course companies. It is an introduction to models for the energy markets with practical examples so is both an educational reference book and a working manual.
1.1 Summary statistics: average and standard deviation
1.2 The histogram
1.3 Summary statistics: skewness and kurtosis
1.4 Distribution functions
1.4.1 The normal distribution
1.4.2 The Student-t distribution
1.4.3 Goodness of fit
1.5 Why do we need models if we have distributions?
1.6 Some literature and software references
2.1 What to model: actual prices or log prices?
2.3 Parameter estimation
2.3.1 Maximum likelihood
2.3.2 Test statistics and robustness
2.3.3 Autocorrelation patterns
2.4 Concluding remarks
3 Standard models for prices and volatility
3.1 Characteristics of energy prices
3.2 Mean-reversion models for energy prices
3.2.2 Hourly electricity prices
3.3 Measuring volatility
3.3.1 Standard deviation
3.3.2 Time varying volatility: GARCH
3.3.3 Time varying volatility: EWMA
3.3.4 Analyzing the out-of-sample performance of volatility
3.4 Concluding remarks
4 Beyond mean-reversion
4.1 Modelling price spikes
4.1.1 Why spikes in power markets occur
4.1.2 Jump models
4.1.3 Jump reversion
4.1.4 A regime-switching models for spikes
4.1.5 Advanced regime-switching models
4.1.6 Some other applications of regime switching models
4.2 Concluding remarks
5 Factor models for forward prices
5.1 The information embedded in forward prices
5.2 Factor models
5.3 The Kalman Filter
5.3.1 Example: a time-varying trend parameter
5.4 Estimating the parameters in a long-term short-term model
5.5 Any other factors?
5.6 Concluding remarks
6 Extreme value theory
6.1 Estimation procedure for the tail index
6.2 Risk management
6.3 Concluding remarks
7 Methods for valuing real options
7.1 Real options in energy contracts and real assets
7.2 Black-Scholes related formulas
7.3 A power plant as an option
7.4 Option valuation with trees
7.5 Incorporating operational constraints
7.6 Least Squares Monte Carlo
7.7 Concluding remarks