Financial signal processing and machine learning:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
West Sussex, United Kingdom
Wiley
2016
|
| Schlagworte: | |
| Online-Zugang: | FRO01 UBG01 Volltext Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 online resource |
| ISBN: | 9781118745540 111874554X 9781118745632 1118745639 |
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Datensatz im Suchindex
| _version_ | 1804176447564152832 |
|---|---|
| adam_text | FINANCIAL SIGNAL
PROCESSING AND
MACHINE LEARNING
Edited by
Ali N Akansu
New Jersey Institute of Technology, USA
Sanjeev R Kulkarni
Princeton University, USA
Dmitry Malioutov
IBM T J Watson Research Center, USA
IEEE PRESS
Wiley
Contents
List of Contributors xiii
Preface xv
1 Overview 1
Ali N Akansu, Sanjeev R Kulkami, and Dmitry Malioutov
1 1 Introduction 1
12A Bird’s-Eye View of Finance 2
121 Trading and Exchanges 4
122 Technical Themes in the Book 5
1 3 Overview of the Chapters 6
131 Chapter 2: “Sparse Markowitz Portfolios ” by Christine De Mol 6
132 Chapter 3: “Mean-Reverting Portfolios: Tradeoffs between Sparsity
and Volatility” by Marco Cuturi and Alexandre d’Aspremont 7
133 Chapter 4: “Temporal Causal Modeling” by Prabhanjan Kambadur,
Aurelie C Lozano, and Ronny Luss 7
134 Chapter 5: “Explicit Kernel and Sparsity of Eigen Subspace for the
AR( 1) Process” by Mustafa U To run, Onur Yilmaz and Ali N Akansu 1
135 Chapter 6: “Approaches to High-Dimensional Covariance and
Precision Matrix Estimation ” by Jianqing Fan, Yuan Liao, and Han
Liu 1
136 Chapter 7: “Stochastic Volatility: Modeling and Asymptotic
Approaches to Option Pricing and Portfolio Selection ” by Matthew
Lorig and Ronnie Sircar 1
137 Chapter 8: “Statistical Measures of Dependence for Financial Data ”
by David S Matteson, Nicholas A James, and William B Nicholson 8
138 Chapter 9: “Correlated Poisson Processes and Their Applications in
Financial Modeling ” by Alexander Kreinin 8
139 Chapter 10: “CVaR Minimizations in Support Vector Machines” by
Junya Gotoh and Akiko Takeda 8
1 3 10 Chapter 11: “Regression Models in Risk Management” by Stan
Uryasev 8
1 4 Other Topics in Financial Signal Processing and Machine Learning 9
References 9
VI
Contents
2 Sparse Markowitz Portfolios 11
Christine De Mol
2 1 Markowitz Portfolios 11
2 2 Portfolio Optimization as an Inverse Problem: The Need for Regularization 13
2 3 Sparse Portfolios 15
2 4 Empirical Validation 17
2 5 Variations on the Theme 18
251 Portfolio Rebalancing 18
252 Portfolio Replication or Index Tracking 19
253 Other Penalties and Portfolio Norms 19
2 6 Optimal Forecast Combination 20
Acknowlegments 21
References 21
3 Mean-Reverting Portfolios 23
Marco Cuturi and Alexandre d’Aspremont
3 1 Introduction 23
311 Synthetic Mean-Reverting Baskets 24
312 Mean-Reverting Baskets with Sufficient Volatility and Sparsity 24
3 2 Proxies for Mean Reversion 25
321 Related Work and Problem Setting 25
322 Predictability 26
323 Portmanteau Criterion 27
324 Crossing Statistics 28
3 3 Optimal Baskets 28
331 Minimizing Predictability 29
332 Minimizing the Portmanteau Statistic 29
333 Minimizing the Crossing Statistic 29
3 4 Semidefinite Relaxations and Sparse Components 30
341A Semidefinite Programming Approach to Basket Estimation 30
342 Predictability 30
343 Portmanteau 31
344 Crossing Stats 31
3 5 Numerical Experiments 32
351 Historical Data 32
352 Mean-reverting Basket Estimators 33
353 Jurek and Yang (2007) Trading Strategy 33
354 Transaction Costs 33
355 Experimental Setup 36
356 Results 36
3 6 Conclusion 39
References 39
Contents
vii
4 Temporal Causal Modeling 41
Prabhanjan Kambadur, Aurelie C Lozano, and Ronny Luss
4 1 Introduction 41
4 2 TCM 46
421 Granger Causality and Temporal Causal Modeling 46
422 Grouped Temporal Causal Modeling Method 47
423 Synthetic Experiments 49
4 3 Causal Strength Modeling 51
4 4 Quantile TCM (Q-TCM) 52
441 Modifying Group OMP for Quantile Loss 52
442 Experiments 53
4 5 TCM with Regime Change Identification 55
451 Model 56
452 Algorithm 58
453 Synthetic Experiments 60
454 Application: Analyzing Stock Returns 62
4 6 Conclusions 63
References 64
5 Explicit Kernel and Sparsity of Eigen Subspace for the
AR(1) Process 67
Mustafa U Torun, Onur Yilmaz, and Ali N Akansu
5 1 Introduction 67
5 2 Mathematical Definitions 68
521 Discrete AR( 1) Stochastic Signal Model 68
522 Orthogonal Subspace 69
5 3 Derivation of Explicit KLT Kernel for a Discrete AR( 1) Process 72
531A Simple Method for Explicit Solution of a Transcendental
Equation 73
532 Continuous Process with Exponential Autocorrelation 74
533 Eigenanalysis of a Discrete AR( 1) Process 76
534 Fast Derivation of KLT Kernel for an AR( 1) Process 79
5 4 Sparsity of Eigen Subspace 82
541 Overview of Sparsity Methods 83
542 pdf-Optimized Midtread Quantizer 84
543 Quantization of Eigen Subspace 86
544 pdf of Eigenvector 87
545 Sparse KLT Method 89
546 Sparsity Performance 91
5 5 Conclusions 97
References 97
viii
Contents
6 Approaches to High-Dimensional Covariance and Precision
Matrix Estimations 100
Jianqing Fan, Yuan Liao, and Han Liu
6 1 Introduction 100
6 2 Covariance Estimation via Factor Analysis 101
621 Known Factors 103
622 Unknown Factors 104
623 Choosing the Threshold 105
624 Asymptotic Results 105
625A Numerical Illustration 107
6 3 Precision Matrix Estimation and Graphical Models 109
631 Column-wise Precision Matrix Estimation 110
632 The Need for Tuning-insensitive Procedures 111
633 TIGER: A Tuning-insensitive Approach for Optimal Precision
Matrix Estimation 112
634 Computation 114
635 Theoretical Properties of TIGER 114
636 Applications to Modeling Stock Returns 115
637 Applications to Genomic Network 118
6 4 Financial Applications 119
641 Estimating Risks of Large Portfolios 119
642 Large Panel Test of Factor Pricing Models 121
6 5 Statistical Inference in Panel Data Models 126
651 Efficient Estimation in Pure Factor Models 126
652 Panel Data Model with Interactive Effects 127
653 Numerical Illustrations 130
6 6 Conclusions 131
References 131
7 Stochastic Volatility 135
Matthew Lorig and Ronnie Sircar
7 1 Introduction 135
711 Options and Implied Volatility 136
712 Volatility Modeling 137
7 2 Asymptotic Regimes and Approximations 141
721 Contract Asymptotics 142
722 Model Asymptotics 142
723 Implied Volatility Asymptotics 143
724 Tractable Models 145
725 Model Coefficient Polynomial Expansions 146
726 Small “Vol ofVol” Expansion 152
727 Separation of Timescales Approach 152
728 Comparison of the Expansion Schemes 154
7 3 Merton Problem with Stochastic Volatility: Model Coefficient Polynomial
Expansions 155
Contents
ix
731 Models and Dynamic Programming Equation 155
732 Asymptotic Approximation 157
733 Power Utility 159
7 4 Conclusions 160
Acknowledgements 160
References 160
8 Statistical Measures of Dependence for Financial Data 162
David S Matteson, Nicholas A James, and William B Nicholson
8 1 Introduction 162
8 2 Robust Measures of Correlation and Autocorrelation 164
821 Transformations and Rank-Based Methods 166
822 Inference 169
823 Misspecification Testing 171
8 3 Multivariate Extensions 174
831 Multivariate Volatility 175
832 Multivariate Misspecification Testing 176
833 Granger Causality 176
834 Nonlinear Granger Causality 111
8 4 Copulas 179
841 Fitting Copula Models 180
842 Parametric Copulas 181
843 Extending beyond Two Random Variables 183
844 Software 185
8 5 Types of Dependence 185
851 Positive and Negative Dependence 185
852 Tail Dependence 187
References 188
9 Correlated Poisson Processes and Their Applications in Financial
Modeling 191
Alexander Kreinin
9 1 Introduction 191
9 2 Poisson Processes and Financial Scenarios 193
921 Integrated Market-Credit Risk Modeling 193
922 Market Risk and Derivatives Pricing 194
923 Operational Risk Modeling 194
924 Correlation of Operational Events 195
9 3 Common Shock Model and Randomization of Intensities 196
931 Common Shock Model 196
932 Randomization of Intensities 196
9 4 Simulation of Poisson Processes 197
941 Forward Simulation 197
942 Backward Simulation 200
9 5 Extreme Joint Distribution 207
X
Contents
951 Reduction to Optimization Problem 207
952 Monotone Distributions 208
953 Computation of the Joint Distribution 214
954 On the Frechet-Hoejfding Theorem 215
955 Approximation of the Extreme Distributions 217
9 6 Numerical Results 219
961 Examples of the Support 219
962 Correlation Boundaries 221
9 7 Backward Simulation of the Poisson-Wiener Process 222
9 8 Concluding Remarks 227
Acknowledgments 228
Appendix A 229
A 1 Proof of Lemmas 9 2 and 9 3 229
A11 Proof of Lemma 9 2 229
A12 Proof of Lemma 9 3 230
References 231
10 CVaR Minimizations in Support Vector Machines 233
Jun-ya Gotoh and Akiko Takeda
10 1 What Is CVaR? 234
10 1 1 Definition and Interpretations 234
10 1 2 Basic Properties of CVaR 238
10 1 3 Minimization of CVaR 240
10 2 Support Vector Machines 242
10 2 1 Classification 242
10 2 2 Regression 246
10 3 v-SVMs as CVaR Minimizations 247
10 3 1 v-SVMs as CVaR Minimizations with Homogeneous Loss 247
10 3 2 v-SVMs as CVaR Minimizations with Nonhomogeneous Loss 251
10 3 3 Refining the v-Property 253
10 4 Duality 256
10 4 1 Binary Classification 256
10 4 2 Geometric Interpretation of v-SVM 257
10 4 3 Geometric Interpretation of the Range of v for v-SVC 258
10 4 4 Regression 259
10 4 5 One-class Classification and SVDD 259
10 5 Extensions to Robust Optimization Modelings 259
10 5 1 Distributionally Robust Formulation 259
10 5 2 Measurement-wise Robust Formulation 261
10 6 Literature Review 262
10 6 1 CVaR as a Risk Measure 263
10 6 2 From CVaR Minimization to SVM 263
10 6 3 From SVM to CVaR Minimization 263
10 6 4 Beyond CVaR 263
References 264
Contents xi
11 Regression Models in Risk Management 266
Stan Uryasev
11 1 Introduction 267
11 2 Error and Deviation Measures 268
11 3 Risk Envelopes and Risk Identifiers 271
11 3 1 Examples of Deviation Measures D, Corresponding Risk Envelopes
Q, and Sets of Risk Identifiers QV(X) 272
11 4 Error Decomposition in Regression 273
11 5 Least-Squares Linear Regression 275
11 6 Median Regression 277
11 7 Quantile Regression and Mixed Quantile Regression 281
11 8 Special Types of Linear Regression 283
11 9 Robust Regression 284
References, Further Reading, and Bibliography 287
Index
|
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| spelling | Akansu, Ali N. 1958- edt Financial signal processing and machine learning edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov West Sussex, United Kingdom Wiley 2016 1 online resource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index COMPUTERS / General bisacsh Machine learning fast Signal processing / Digital techniques fast Machine learning Signal processing / Digital techniques Kulkarni, Sanjeev edt Malioutov, Dmitry edt https://onlinelibrary.wiley.com/doi/book/10.1002/9781118745540 Verlag URL des Erstveröffentlichers Volltext HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029093466&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Financial signal processing and machine learning |
| title_auth | Financial signal processing and machine learning |
| title_exact_search | Financial signal processing and machine learning |
| title_full | Financial signal processing and machine learning edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov |
| title_fullStr | Financial signal processing and machine learning edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov |
| title_full_unstemmed | Financial signal processing and machine learning edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov |
| title_short | Financial signal processing and machine learning |
| title_sort | financial signal processing and machine learning |
| topic | COMPUTERS / General bisacsh Machine learning fast Signal processing / Digital techniques fast Machine learning Signal processing / Digital techniques |
| topic_facet | COMPUTERS / General Machine learning Signal processing / Digital techniques |
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