Energy Risk Modelling: Applied Modelling Methods for Risk Managers

✍ Scribed by Nigel Da Costa Lewis

• Publisher: Palgrave Macmillan
• Year: 2005
• Tongue: English
• Leaves: 268
• Series: Finance and Capital Markets
• Category: Library

✦ Synopsis

This book is a practitioner's guide for readers who already have a basic understanding of risk management. Statistical ideas are presented by detailing the necessary concepts and outlining how these methods can be implemented. The book differentiates itself from other energy risk books on the market by providing practical examples of how statistical methods are used to solve issues faced in energy risk management.

✦ Table of Contents

Cover......Page 1
Contents......Page 8
List of Tables......Page 12
List of Figures......Page 14
Preface......Page 19
1 The Statistical Nature of Energy Risk Modeling......Page 22
1.1 Historical evolution of energy......Page 23
1.2 Financial risks of energy......Page 25
1.3 The role of economics–elements of price theory......Page 27
1.4 Energy markets and products......Page 34
1.5 The science of energy risk modeling......Page 36
1.6 Further reading and resources......Page 37
Part I Statistical Foundations of Energy Risk Modeling......Page 40
2.1 Describing random events......Page 42
2.2 What is probability?......Page 44
2.3 Probability functions......Page 50
2.4 The normal distribution......Page 55
2.5 Relevance of probability for energy risk management......Page 57
2.6 A probabilistic model for energy price risk......Page 59
2.7 Summary......Page 60
2.9 Review questions......Page 61
3.1 Measures of central tendency......Page 62
3.2 Measures of dispersion......Page 65
3.3 A normally distributed model for energy price risk......Page 67
3.4 Measures of shape......Page 68
3.5 Relevance of descriptive statistics......Page 71
3.6 Summary......Page 72
3.8 Review questions......Page 73
4.1 What is a hypothesis?......Page 74
4.2 What is the point of hypothesis testing?......Page 75
4.3 Refuting chance and controlling errors......Page 76
4.4 A step by step guide to conducting a hypothesis test......Page 77
4.6 Summary......Page 82
4.8 Review questions......Page 83
Part II Applied Modeling: Techniques and Applications......Page 84
5.1 Developing a simple model for energy returns......Page 86
5.2 Using descriptive statistics to assess the model......Page 88
5.3 Using inferential statistics to aid model construction......Page 94
5.4 What to do when normality fails?......Page 96
5.5 Building models using mixture distributions......Page 105
5.6 General approaches for estimating parameters......Page 111
5.7 Summary......Page 122
5.8 Further reading......Page 126
5.9 Review questions......Page 127
6 Nonparametric Density Estimation for Energy Price Returns......Page 128
6.1 Describing energy price data with histograms......Page 129
6.2 Kernel density estimation......Page 136
6.3 Explaining empirical distributions to nonstatistical people......Page 141
6.5 Review questions......Page 144
7.1 Understanding correlation......Page 146
7.2 Correlation and hedging......Page 149
7.3 Pearson product moment correlation coefficient......Page 150
7.4 Spearman rank correlation coefficient......Page 153
7.5 Spurious correlation......Page 154
7.7 Confidence intervals for the correlation coefficient......Page 156
7.8 Hypothesis tests of the correlation coefficient......Page 157
7.9 Coefficient of determination......Page 162
7.10 Time evolution of correlation coefficients......Page 163
7.11 Other measures of correlation......Page 164
7.12 Causation, dependence, and correlation......Page 169
7.13 Summary......Page 170
7.15 Review questions......Page 171
8.1 The simple linear regression model......Page 172
8.2 Expectation and regression......Page 173
8.3 Parameter estimation......Page 177
8.4 Assessing the simple linear regression model......Page 181
8.7 Review questions......Page 189
9.1 The multiple regression model......Page 190
9.2 Assessing the multiple regression model......Page 191
9.4 Building and estimating multiple linear regression models in R......Page 192
9.5 Multivariate regression......Page 195
9.7 Further reading......Page 198
9.8 Review questions......Page 199
10.1 Assumptions of linear regression......Page 200
10.2 Linearity......Page 201
10.5 Independent variables uncorrelated......Page 202
10.7 Misspecification testing using R......Page 203
10.8 Summary......Page 206
10.10 Review questions......Page 207
11.1 Polynomial regression......Page 208
11.3 Non-linear regression modeling using R......Page 209
11.4 Logistic and other limited dependent regression models......Page 211
11.7 Review questions......Page 216
12.1 The constant volatility model......Page 217
12.2 Exponentially weighted moving average models......Page 221
12.3 Generalized autoregressive conditional hetroscedasticity models......Page 226
12.5 Further reading......Page 231
12.6 Review questions......Page 233
13.1 What are stochastic differential equations?......Page 234
13.2 Dealing with jumps in energy prices......Page 239
13.3 Modeling mean reversion......Page 242
13.4 Introducing stochastic volatility into energy prices......Page 244
13.6 Further reading......Page 246
13.7 Review questions......Page 247
Appendix: Statistical Tables......Page 248
Notes......Page 259
C......Page 263
F......Page 264
L......Page 265
P......Page 266
S......Page 267
W......Page 268