Quantitative Portfolio Optimization | Buch

Cover: 9781394281312 | Quantitative Portfolio Optimization | Miquel Noguer Alonso (u. a.)

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

90,25 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 1-2 Wochen

Kategorien:

Der Artikel ist nicht in deutscher Sprache verfasst.
Die Sprache der Produktbeschreibung und Details kann in Englisch angegeben sein. Sollten Sie Verständnisprobleme haben, wenden Sie sich gerne an uns.

Beschreibung

PRAISE FOR
QUANTITATIVE PORTFOLIO OPTIMIZATIONOPTIMIZATION

"This book provides an excellent exposition on portfolio optimization, serving not only as a self-contained guide to this important topic, but also modernizing the field with the latest advances in battle-tested machine learning approaches. The book is well structured and application centric. This is a must read for every quantitative portfolio manager."
- Matthew Dixon, FRM, Ph.D., Associate Professor of Applied Math at the Illinois Institute of Technology and an Affiliate Associate Professor of the Stuart School of Business

"Quantitative Portfolio Optimization: Advanced Techniques and Applications is an essential guide for anyone seeking to navigate the complex world of modern portfolio management. This book masterfully blends the foundational principles of portfolio theory with cutting-edge advancements in risk management, dynamic models, and control systems. Its integration of machine learning and deep learning offers readers a forward-looking perspective on leveraging AI-driven techniques for optimization. What truly sets this book apart is its comprehensive approach. From theoretical insights to practical backtesting applications, it equips professionals, researchers, and students with the tools to design and refine robust investment strategies. Whether you're delving into the nuances of risk modelling or exploring dynamic portfolio control with the latest AI methodologies, this text is an invaluable resource. This book isn't just about managing portfolios-it's about mastering the art and science behind it. Highly recommended for anyone aiming to achieve excellence in quantitative finance and portfolio optimization."
-Daniel Bloch, Director, Quant Finance Limited

PRAISE FOR
QUANTITATIVE PORTFOLIO OPTIMIZATIONOPTIMIZATION

Inhaltsverzeichnis

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

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

Foundations of Portfolio Theory

CHAPTER 3

Modern Portfolio Theory 35

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

Bayesian Methods in Portfolio Optimization 73

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

Risk Management

CHAPTER 5

Risk Models and Measures 107

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

Factor Models and Factor Investing 125

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

CHAPTER 7

Market Impact, Transaction Costs, and Liquidity 145

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

Dynamic Models and Control

CHAPTER 8

Optimal Control 171

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 Schödinger Control in Wealth Management 185

8.7 Notes 187

CHAPTER 9

Markov Decision Processes 189

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

CHAPTER 10

Reinforcement Learning 209

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

Machine Learning and Deep Learning

CHAPTER 11

Deep Learning in Portfolio Management 253

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

CHAPTER 12

Graph-based Portfolios 285

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

Sensitivity-based Portfolios 295

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

Backtesting

CHAPTER 14

Backtesting in Portfolio Management 319

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

CHAPTER...

Details

Erscheinungsjahr:	2025
Fachbereich:	Betriebswirtschaft
Genre:	Importe , Wirtschaft
Rubrik:	Recht & Wirtschaft
Medium:	Buch
Inhalt:	Einband - fest (Hardcover)
ISBN-13:	9781394281312
ISBN-10:	1394281315
Sprache:	Englisch
Einband:	Gebunden
Autor:	Noguer Alonso, Miquel Antolin Camarena, Julian Bueno Guerrero, Alberto
Hersteller:	Wiley
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	234 x 162 x 28 mm
Von/Mit:	Miquel Noguer Alonso (u. a.)
Erscheinungsdatum:	29.01.2025
Gewicht:	0,694 kg

Artikel-ID: 129243779

Inhaltsverzeichnis

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

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

Foundations of Portfolio Theory

CHAPTER 3

Modern Portfolio Theory 35

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

Bayesian Methods in Portfolio Optimization 73

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

Risk Management

CHAPTER 5

Risk Models and Measures 107

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

Factor Models and Factor Investing 125

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

CHAPTER 7

Market Impact, Transaction Costs, and Liquidity 145

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

Dynamic Models and Control

CHAPTER 8

Optimal Control 171

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 Schödinger Control in Wealth Management 185

8.7 Notes 187

CHAPTER 9

Markov Decision Processes 189

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

CHAPTER 10

Reinforcement Learning 209

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

Machine Learning and Deep Learning

CHAPTER 11

Deep Learning in Portfolio Management 253

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

CHAPTER 12

Graph-based Portfolios 285

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

Sensitivity-based Portfolios 295

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

Backtesting

CHAPTER 14

Backtesting in Portfolio Management 319

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

CHAPTER...

Details

Erscheinungsjahr:	2025
Fachbereich:	Betriebswirtschaft
Genre:	Importe , Wirtschaft
Rubrik:	Recht & Wirtschaft
Medium:	Buch
Inhalt:	Einband - fest (Hardcover)
ISBN-13:	9781394281312
ISBN-10:	1394281315
Sprache:	Englisch
Einband:	Gebunden
Autor:	Noguer Alonso, Miquel Antolin Camarena, Julian Bueno Guerrero, Alberto
Hersteller:	Wiley
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	234 x 162 x 28 mm
Von/Mit:	Miquel Noguer Alonso (u. a.)
Erscheinungsdatum:	29.01.2025
Gewicht:	0,694 kg

Artikel-ID: 129243779

Sicherheitshinweis

Ähnliche Produkte