This book introduces empirical methods for analyzing energy markets. Even beginners in econometrics and mathematical finance must be able to learn how to utilize these methodologies and how to interpret the analysis results. This book provides some example analyses of the North American, European, and Asian energy markets. The reader will experience some theories and practices of energy trading and risk management. This book reveals the characteristics of energy markets using quantitative analyses. Examples include unit root, cointegration, long-term equilibrium, stochastic arbitrage simulation, multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models, exponential GARCH (EGARCH) models, optimal hedge ratio, copula, value-at-risk (VaR), expected shortfall, vector autoregressive (VAR) models, vector moving average (VMA) models, connectedness, and frequency decomposition. This book is suitable for people interested in the empirical study of energy markets and energy trade.
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1 | (4) |
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3 | (2) |
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2 Arbitrage Trading in Energy Markets and Measuring Its Risk |
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5 | (48) |
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5 | (2) |
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2.2 Data and Preliminary Analyses |
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7 | (13) |
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2.2.1 Descriptive Statistics |
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2.2.2 Stationary and Unit Root Test |
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16 | (4) |
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2.2.4 Long-Term Equilibrium Estimation |
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2.3.1 Arbitrage Between Own Spot Spread and Future Spread |
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2.3.2 Statistical Arbitrage |
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2.5 Risk Measurement in Statistical Arbitrage |
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2.5.1 Value-At-Risk and Expected Shortfall |
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31 | (10) |
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2.5.3 Copula Estimation and Risk Measurement |
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41 | (3) |
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44 | (5) |
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49 | (4) |
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3 Fuel Market Connectedness and Fuel Portfolio Risk |
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53 | (32) |
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53 | (1) |
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54 | (3) |
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55 | (1) |
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56 | (1) |
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57 | (5) |
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3.3.1 Connectedness Index (Diebold and Yilmaz [ 4]) |
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58 | (2) |
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3.3.2 Spectral Decomposition (Barunik and Krehlik [ 1]) |
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60 | (1) |
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3.3.3 EGARCH Volatility Series Estimation |
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61 | (1) |
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62 | (19) |
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63 | (8) |
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71 | (10) |
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81 | (2) |
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83 | (2) |
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4 Hedging Strategy with Futures Contracts |
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85 | (20) |
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85 | (2) |
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87 | (2) |
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4.3 Optimal Hedge Ratio and Hedge Effectiveness |
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4.4 Multivariate GARCH Model |
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4.4.1 Diagonal VECH Model |
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4.4.2 Diagonal BEKK Model |
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93 | (1) |
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94 | (8) |
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94 | (4) |
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98 | (4) |
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102 | (2) |
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104 | (1) |
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5 Market Risk of a Power Generation Business |
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105 | (18) |
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105 | (2) |
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107 | (1) |
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5.3 Data and Preliminary Analyses |
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107 | (7) |
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107 | (1) |
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108 | (2) |
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110 | (4) |
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114 | (5) |
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114 | (1) |
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115 | (4) |
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119 | (1) |
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119 | (3) |
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122 | (1) |
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6 Alternative to Postface: Market Risk Transfer in Power Companies |
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123 | (8) |
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129 | (2) |
| Index |
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131 | |
Dr. Shigeyuki Hamori is a Professor of Economics at Kobe University in Japan. He received a Ph.D. from Duke University. He is the President of the International Research Institute for Economics and Management, the Distinguished Fellow of the International Engineering and Technology Institute (DFIETI), and the Distinguished Fellow of the Institute of Data Science and Artificial Intelligence (DFIDSAI). His main research interests are applied time series analysis, empirical finance, data science, and international finance. He is the Co-Editor of the Singapore Economic Review, the Associate Editor of the International Review of Financial Analysis, and the Associated Editor of the Eurasian Economic Review. He served as the Editor of special issues of various journals such as Frontiers in Environmental Science, Energies, Emerging Market Finance and Trade, and Journal of Risk and Financial Management. He has published about 250 articles in international peer-reviewed journals and 20books from Springer, Routledge, World Scientific, etc.
Dr. Tadahiro Nakajima is a senior researcher at the Kansai Electric Power Company, Incorporated. He received a Ph.D. from Kobe University. He revceived the Highly Commended Paper of the Studies in Economic and Finance Literati Award (Emeral Publishing). His main research interests are applied time series analysis, energy economics and energy markets. He has published about 20 articles in international peer-reviewes journals and a book from Springer.
Lisainfo e-raamatute kohta