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· 제목 : Quantitative Portfolio Optimization: Advanced Techniques and Applications (Hardcover)
· 분류 : 외국도서 > 경제경영 > 투자/증권 > 금융자산 관리
· ISBN : 9781394281312
· 쪽수 : 384쪽
· 출판일 : 2025-01-29
· 분류 : 외국도서 > 경제경영 > 투자/증권 > 금융자산 관리
· ISBN : 9781394281312
· 쪽수 : 384쪽
· 출판일 : 2025-01-29
목차
Contents
?
Preface? xiii
Acknowledgements? xv
About the Authors? xvii
?
CHAPTER? 1
?
Introduction? 1
?
1.1 Evolution of Portfolio Optimization 1
1.2 Role of Quantitative Techniques 1
1.3 Organization of the Book 4
Contents
Preface? xiii
Acknowledgements? xv
About the Authors? xvii
CHAPTER? 1
?
Introduction? 1
?
1.1 Evolution of Portfolio Optimization 1
1.2 Role of Quantitative Techniques 1
1.3 Organization of the Book 4
?
CHAPTER? 2
?
History of Portfolio Optimization 7
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2.1 Early beginnings 7
2.2 Harry Markowitz’s Modern Portfolio Theory (1952) 9
2.3 Black-Litterman Model (1990s) 13
2.4 Alternative Methods: Risk Parity, Hierarchical Risk Parity and Machine Learning? 19
? ? ?2.4.1 Risk Parity? 19
? ? ?2.4.2 Hierarchical Risk Parity? 26
? ? ?2.4.3 Machine Learning? 27
2.5 Notes? 31
PART ONE
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Foundations of Portfolio Theory
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CHAPTER 3
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Modern Portfolio Theory? 35
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3.1 Efficient Frontier and Capital Market Line? 35
? ? ? 3.1.1 Case Without Riskless Asset? 35
? ? ? 3.1.2 Case With a Riskless Asset? 41
3.2 Capital Asset Pricing Model? 48
? ? ? 3.2.1 Case Without Riskless Asset? 48
? ? ? 3.2.2 Case With a Riskless Asset? 52
3.3 Multifactor Models? 54
3.4 Challenges of Modern Portfolio Theory? 59
? ? ? 3.4.1 Estimation Techniques in Portfolio Allocation? 60
? ? ? 3.4.2 Non-Elliptical Distributions and Conditional Value-at-Risk (CVaR)? 63
3.5 Quantum Annealing in Portfolio Management? 65
3.6 Mean-Variance Optimization with CVaR Constraint? 67
? ? ? 3.6.1 Problem Formulation? 67
? ? ? 3.6.2 Optimization Problem? 68
? ? ? 3.6.3 Clarification of Optimization Classes? 68
? ? ? 3.6.4 Numerical Example? 69
3.7 Notes? 70
CHAPTER? 4
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Bayesian Methods in Portfolio Optimization? ?73
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4.1 The Prior? 75
4.2 The Likelihood? 79
4.3 The Posterior? 80
4.4 Filtering? 83
4.5 Hierarchical Bayesian Models? 87
4.6 Bayesian Optimization? 89
? ? ? 4.6.1 Gaussian Processes in a Nutshell? 90
? ? ?4.6.2 Uncertainty Quantification and Bayesian Decision Theory? 94
4.7 Applications to Portfolio Optimization? 96
? ? ?4.7.1 GP Regression for Asset Returns? 96
? ? ?4.7.2 Decision Theory in Portfolio Optimization? 96
? ? ?4.7.3 The Black-Litterman Model? 99
4.8 Notes? 103
PART TWO
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Risk Management
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CHAPTER 5
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Risk Models and Measures? 107
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5.1 Risk Measures? 107
5.2 VaR and CVaR? 109
? ? ? 5.2.1 VaR? 110
? ? ?5.2.2 CVaR? 112
5.3 Estimation Methods? 116
? ? ?5.3.1 Variance-Covariance Method? 116
? ? ?5.3.2 Historical Simulation? 116
? ? ?5.3.3 Monte Carlo Simulation? 117
5.4 Advanced Risk Measures: Tail Risk and Spectral Measures? 118
? ? ?5.4.1 Tail Risk Measures? 118
? ? ?5.4.2 Spectral Measures? 120
5.5 Notes 123
CHAPTER 6
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Factor Models and Factor Investing? 125
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6.1 Single and Multifactor Models? 126
? ? ? 6.1.1 Statistical Models? 127
? ? ? 6.1.2 Macroeconomic Models? 128
? ? ? 6.1.3 Cross-sectional Models? 130
6.2 Factor Risk and Performance Attribution? 135
6.3 Machine Learning in Factor Investing? 141
6.4 Notes? 144
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CHAPTER 7
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Market Impact, Transaction Costs, and Liquidity? 145
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7.1 Market Impact Models? 145
7.2 Modeling Transaction Costs? 148
? ? ? 7.2.1 Single Asset? 151
? ? ? 7.2.2 Multiple Assets? 154
7.3 Optimal Trading Strategies? 155
? ? ? 7.3.1 Mei, DeMiguel, and Nogales (2016)? 156
? ? ? 7.3.2 Skaf and Boyd (2009)? 159
7.4 Liquidity Considerations in Portfolio Optimization? 161
? ? ? 7.4.1 MV and Liquidity? 162
? ? ? 7.4.2 CAPM and Liquidity? 163
? ? ? 7.4.3 APT and Liquidity? 165
7.5 Notes? 167
PART THREE
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Dynamic Models and Control
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CHAPTER 8
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Optimal Control? 171
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8.1 Dynamic Programming? 171
8.2 Approximate Dynamic Programming? 171
8.3 The Hamilton-Jacobi-Bellman Equation? 172
8.4 Sufficiently Smooth Problems? 174
8.5 Viscosity Solutions? 176
8.6 Applications to Portfolio Optimization? 180
? ? ? 8.6.1 Classical Merton Problem? 180
? ? ? 8.6.2 Multi-asset Portfolio with Transaction Costs? 181
? ? ? 8.6.3 Risk-sensitive Portfolio Optimization? 183
? ? ? 8.6.4 Optimal Portfolio Allocation with Transaction Costs? 184
? ? ? 8.6.5 American Option Pricing? 184
? ? ? 8.6.6 Portfolio Optimization with Constraints? 184
? ? ? 8.6.7 Mean-variance Portfolio Optimization? 185
? ? ? 8.6.8 Schodinger Control in Wealth Management? 185
8.7 Notes? 187
CHAPTER 9
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Markov Decision Processes? 189
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9.1 Fully Observed MDPs? 191
9.2 Partially Observed MDPs? 192
9.3 Infinite Horizon Problems? 194
9.4 Finite Horizon Problems? 198
9.5 The Bellman Equation? 200
9.6 Solving the Bellman Equation? 203
9.7 Examples in Portfolio Optimization? 205
? ? ? 9.7.1 An MDP in Multi-asset Allocation with Transaction Costs? 205
? ? ? 9.7.2 A POMDP for Asset Allocation with Regime Switching? 205
? ? ? 9.7.3 An MDP with Continuous State and Action Spaces for Option Hedging with Stochastic Volatility? 206
9.8 Notes? 207
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CHAPTER? 10
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Reinforcement Learning? 209
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10.1 Connections to Optimal Control? 211
? ? ? ?10.1.1 Policy Iteration? 212
? ? ? ?10.1.2 Value Iteration? 214
? ? ? ?10.1.3 Continuous vs. Discrete Formulations? 215
10.2 The Environment and The Reward Function? 217
? ? ? ? ?10.2.1 The Environment? 217
? ? ? ? ?10.2.2 The Reward Function? 220
10.3 Agents Acting in an Environment? 223
10.4 State-Action and Value Functions? 225
? ? ? ? 10.4.1 Value Functions? 226
? ? ? ? 10.4.2 Gradients and Policy Improvement? 227
10.5 The Policy? 230
10.6 On-Policy Methods? 233
10.7 Off-Policy Methods? 235
10.8 Applications to Portfolio Optimization? 238
? ? ? ?10.8.1 Mean-variance Optimization? 238
? ? ? ?10.8.2 Reinforcement Learning Comparison with Mean-variance Optimization? 239
? ? ? ?10.8.3 G-Learning and GIRL? 241
? ? ? ?10.8.4 Continuous-time Penalization in Portfolio Optimization? 244
? ? ? ?10.8.5 Reinforcement Learning for Utility Maximization? 246
? ? ? ?10.8.6 Continuous-time Portfolio Optimization with Transaction Costs? 246
10.9 Notes? 247
PART FOUR
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Machine Learning and Deep Learning
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CHAPTER 11
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Deep Learning in Portfolio Management? 253
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11.1 Neurons and Activation Functions? 253
11.2 Neural Networks and Function Approximation? 256
11.3 Review of Some Important Architectures? 259
11.4 Physics-Informed Neural Networks? 269
11.5 Applications to Portfolio Optimization? 276
? ? ? ? 11.5.1 Dynamic Asset Allocation Using the Heston Model? 276
? ? ? ? 11.5.2 Option-Based Portfolio Insurance Using the Bates Model? 277
? ? ? ? 11.5.3 Factor Learning Approach to Generative Modeling of Equities? 278
11.6 The Case for and Against Deep Learning? 280
11.7 Notes? 282
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CHAPTER 12
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Graph-based Portfolios? 285
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12.1 Graph Theory-Based Portfolios? 285
? ? ? ?12.1.1 Literature Review? 285
12.2 Graph Theory Portfolios: MST and TMFG? 285
? ? ? ?12.2.1 Equations and Formulas? 286
? ? ? ?12.2.2 Results? 287
12.3 Hierarchical Risk Parity? 289
12.4 Notes? 294
CHAPTER 13
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Sensitivity-based Portfolios? 295
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13.1 Modeling Portfolios Dynamics with PDEs? 296
13.2 Optimal Drivers Selection: Causality and Persistence? 297
13.3 AAD Sensitivities Approximation? 303
? ? ? ? 13.3.1 Optimal Network Selection? 304
? ? ? ? 13.3.2 Sensitivity Analysis? 304
? ? ? ? 13.3.3 Sensitivity Distance Matrix? 304
13.4 Hierarchical Sensitivity Parity? 307
13.5 Implementation? 307
? ? ? ? 13.5.1 Datasets? 307
? ? ? ? 13.5.2 Experimental Setup? 308
? ? ? ? 13.5.3 Short-to-medium Investments? 309
? ? ? ? 13.5.4 Long-term Investments? 312
13.6 Conclusion? 315
PART FIVE
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Backtesting
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CHAPTER? 14
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Backtesting in Portfolio Management? 319
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14.1 Introduction? 319
14.2 Data Preparation and Handling? 319
14.3 Implementation of Trading Strategies? 320
14.4 Types of Backtests? 321
? ? ? ? 14.4.1 Walk-forward Backtest? 321
? ? ? ? 14.4.2 Resampling Method? 321
? ? ? ? 14.4.3 Monte Carlo Simulations and Generative Models? 321
14.5 Performance Metrics? 322
14.6 Avoiding Common Pitfalls? 323
14.7 Advanced Techniques? 323
14.8 Case Study: Applying Backtesting to a Real-World Strategy? 324
14.9 Impact of Market Conditions on Backtest Results? 324
14.10 Integration with Portfolio Management? 325
14.11 Tools and Software for Backtesting? 325
14.12 Regulatory Considerations? 326
14.13 Conclusion? 326
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CHAPTER? 15
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Scenario Generation? 329
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15.1 Historical Scenarios? 329
15.2 Bootstrapping Scenarios? 330
15.3 Copula-Based Scenarios? 330
15.4 Risk Factor Model-Based Scenarios? 330
15.5 Time Series Model Scenarios? 331
15.6 Variational Autoencoders? 331
15.7 Generative Adversarial Networks (GANs)? 332
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Appendix 333
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A.1 Software and Tools for Portfolio Optimization? 333
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Bibliography? 335
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Index? 357
