دانلود کتاب Multi-factor Models and Signal Processing Techniques: Application to Quantitative Finance

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Cover ......Page 1
Title Page ......Page 5
Contents ......Page 7
Foreword......Page 13
Introduction......Page 17
Notations and Acronyms......Page 23
1.1. Introduction......Page 27
1.2.1. Notations ......Page 28
1.2.2. Factor representation......Page 30
1.3.1. Style analysis ......Page 33
1.3.2. Optimal portfolio allocation......Page 36
1.4.1. Factor selection ......Page 37
1.4.2. Parameters estimation......Page 39
1.5.1. CAPM and Sharpe’s market model......Page 40
1.5.2. APT for arbitrage pricing theory......Page 43
1.6. Glossary Volatility......Page 44
2.1. Introduction......Page 49
2.2. Qualitative know-how......Page 50
2.2.1. Fama and French model......Page 51
2.2.2. The Chen et al. model......Page 52
2.2.3. The risk-based factor model of Fung and Hsieh......Page 53
2.3. Quantitative methods based on eigenfactors......Page 57
2.3.1. Notation......Page 58
2.3.2. Subspace methods: the Principal Component Analysis......Page 59
2.4.1. Information criteria......Page 62
2.5. Appendix 1: Covariance matrix estimation......Page 64
2.5.1. Sample mean......Page 65
2.5.2. Sample covariance matrix......Page 66
2.5.3. Robust covariance matrix estimation: M-estimators......Page 69
2.6. Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm......Page 72
2.7. Appendix 3: Large panel data......Page 74
2.7.1. Large panel data criteria......Page 75
2.8. Chapter 2 highlights ......Page 82
3.1. Introduction......Page 85
3.2.1. Factor model per return ......Page 86
3.2.2. Alpha and beta estimation per return......Page 87
3.3.2. LSE regression......Page 88
3.4. LSE objective and criterion......Page 89
3.5. How LSE is working (for LSE users and programmers)......Page 90
3.6.1. Bias and variance ......Page 91
3.6.2. Geometrical interpretation of LSE......Page 92
3.7. Derivations of LSE solution......Page 96
3.8.1. LSE method does not provide a recursive estimate ......Page 97
3.8.2. The state space model and its recursive component......Page 98
3.8.3. Parsimony and orthogonality assumptions......Page 99
3.9.1. Self-aggregation feature ......Page 100
3.9.3. Innovation property......Page 101
3.10. What is the objective of KF?......Page 102
3.11.1. Algorithm summary ......Page 103
3.11.2. Initialization of the KF recursive equations......Page 106
3.12.1. Prediction filtering, equation [3.34]......Page 107
3.12.2. Prediction accuracy processing, equation [3.35]......Page 108
3.12.3. Correction filtering equations [3.36]–[3.37]......Page 109
3.12.4. Correction accuracy processing, equation [3.38]......Page 110
3.13.1. Comparison of the estimation methods on synthetic data ......Page 112
3.13.2. Market risk hedging given a single-factor model ......Page 118
3.13.3. Hedge fund style analysis using a multi-factor model ......Page 123
3.14.1. Geometrical interpretation of MSE criterion and the MMSE solution ......Page 130
3.14.3. Derivation of the prediction accuracy update ......Page 132
3.14.4. Derivation of the correction filtering update ......Page 133
3.14.5. Derivation of the correction accuracy update ......Page 137
3.15. Highlights ......Page 138
3.16. Appendix: Matrix inversion lemma......Page 142
4.1. Introduction......Page 143
4.3.1. RKF description ......Page 145
4.4.1. rgKF description ......Page 147
4.4.2. rgKF performance......Page 151
4.5. Application to detect irregularities in hedge fund returns......Page 154
4.7. Chapter highlights Extended space-state model......Page 156
Appendix: Some Probability Densities......Page 159
Conclusion......Page 167
Bibliography......Page 169
Index......Page 179