Statistical portfolio estimation:
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| Main Authors: | , , , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla.
CRC Press
[2017]
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | x, 377 Seiten Illustrationen |
| ISBN: | 9781466505605 |
Staff View
MARC
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| 264 | 1 | |a Boca Raton, Fla. |b CRC Press |c [2017] | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
Preface ix
1 Introduction 1
2 Preliminaries 5
2.1 Stochastic Processes and Limit Theorems 5
3 Portfolio Theory for Dependent Return Processes 19
3.1 Introduction to Portfolio Theory 19
3.1.1 Mean-Variance Portfolio 20
3.1.2 Capital Asset Pricing Model 23
3.1.3 Arbitrage Pricing Theory 27
3.1.4 Expected Utility Theory 28
3.1.5 Alternative Risk Measures 33
3.1.6 Copulas and Dependence 36
3.1.7 Bibliographic Notes 38
3.1.8 Appendix 38
3.2 Statistical Estimation for Portfolios 41
3.2.1 Traditional Mean-Variance Portfolio Estimators 42
3.2.2 Pessimistic Portfolio 44
3.2.3 Shrinkage Estimators 46
3.2.4 Bayesian Estimation 48
3.2.5 Factor Models 52
3.2.5.1 Static factor models 52
3.2.5.2 Dynamic factor models 55
3.2.6 High-Dimensional Problems 58
3.2.6.1 The case of m/n —» y e (0, 1) 59
3.2.6.2 The case of m/n — y e (1, оо) 62
3.2.7 Bibliographie Notes 63
3.2.8 Appendix 63
3.3 Simulation Results 65
3.3.1 Quasi-Maximum Likelihood Estimator 66
3.3.2 Efficient Frontier 68
3.3.3 Difference between the True Point and the Estimated Point 69
3.3.4 Inference of fip 70
3.3.5 Inference of Coefficient 72
3.3.6 Bibliographic Notes 73
4 Multiperiod Problem for Portfolio Theory 75
4.1 Discrete Time Problem 76
4.1.1 Optimal Portfol io Weights 76
4.1.2 Consumption Investment 78
VI
CONTENTS
4.1.3 Simulation Approach for VAR(l) model SO
4.1.4 Bibliographic Notes 84
4.2 Continuous Time Problem 84
4.2.1 Optimal Consumption and Portfolio Weights 85
4.2.2 Estimation 90
4.2.2.1 Generalized method of moments (GMM) 91
4.2.2.2 Threshold estimation method 95
4.2.3 Bibliographic Notes 98
4.3 Universa! PortfoHo 98
4.3.1 /¿-Weighted Universal Portfolios 98
4.3.2 Universal Portfolios with Side Information 101
4.3.3 Successive Constant Rebalanced Portfolios 104
4.3.4 Universal Portfolios with Transaction Costs 106
4.3.5 Bibliographic Notes 108
4.3.6 Appendix 109
5 Portfolio Estimation Based on Rank Statistics 113
5.1 Introduction to Rank-Based Statisticsrank 113
5.1.1 History of Ranks 113
5.1.1.1 Wilcoxon’s signed rank and rank sum tests 113
5.1.1.2 Hodges-Lehmann and ChernofF-Savage 117
5.1.2 Maximal Invariantsmaximal invariant 124
5.1.2.1 Invariance of sample space, parameter space and tests 124
5.1.2.2 Most powerful invariant testmost powerful invariant test 125
5.1.3 Efficiencyefficiency of Rank-Based Statistics 126
5.1.3.1 Least favourableleast favourable density and most powerfulmost
powerful test i 26
5.1.3.2 Asymptotically most powerful rank test 129
5.1.4 [/-Statistics for Stationary Processes 137
5.2 Semiparametrically Efficient Estimation in Time Series 139
5.2.1 Introduction to Rank-Based Theory in Time Series 139
5.2.1.1 Testing for randomness against ARMA alternatives 139
5.2.1.2 Testing an ARMA model against other ARMA alternatives 146
5.2.2 Tangent Spacetangent space 149
5.2.3 Introduction to Semiparametric Asymptotic Optimal Theory 155
5.2.4 Semiparametrically Efficient Estimation in Time Series, and Multivariate
Cases 159
5.2.4.1 Rank-based optimal influence functions (univariate case) 159
5.2.4.2 Rank-based optimal estimation for elliptical residuals 164
5.3 Asymptotic Theory of Rank Order Statistics for ARCH Residual Empirical
Processes 170
5.4 Independent Component Analysis 180
5.4.1 Introduction to Independent Component Analysis 180
5.4.1.1 The foregoing model for financial time series 180
5.4.1.2 ICA modeling for financial time series 187
5.4.1.3 ICA modeling in frequency domain for time series 191
5.5 Rank-Based Optimal Portfolio Estimation 202
5.5.1 Portfolio Estimation Based on Ranks for Independent Components 202
5.5.2 Portfolio Estimation Based on Ranks for Elliptical Residualselliptical
residuals 204
CONTENTS vii
6 Portfolio Estimation Influenced by Non-Gaussian Innovations and Exogenous
Variables 207
6.1 Robust Portfolio Estimation under Skew-Normal Return Processes 207
6.2 Portfolio Estimators Depending on Higher-Order Cumulant Spectra 211
6.3 Portfolio Estimation under the Utility Function Depending on Exogenous Variables 215
6.4 Multi-Step Ahead Portfolio Estimation 221
6.5 Causality Analysis 224
6.6 Classificationclassification by Quantile Regressionquantile regression 227
6.7 Portfolio Estimation under Causal Variables 229
7 Numerical Examples 235
7.1 Real Data Analysis for Portfolio Estimation 235
7.1.1 Introduction 235
7.1.2 Data 235
7.1.3 Method 237
7.1.3.1 Model selection 237
7.1.3.2 Confidence region 238
7.1.3.3 Locally stationary estimation 239
7.1.4 Results and Discussion 240
7.1.5 Conclusions 242
7.1.6 Bibliographic Notes 243
7.2 Application for Pension Investment 243
7.2.1 Introduction 244
7.2.2 Data 244
7.2.3 Method 244
7.2.4 Results and Discussion 248
7.2.5 Conclusions 248
7.3 Microarray Analysis Using Rank Order Statistics for ARCH Residual 248
7.3.1 Introduction 249
7.3.2 Data 250
7.3.3 Method 252
7.3.3.1 The rank order statistic for the ARCH residual empirical process 252
7.3.3.2 Two-group comparison for microarray data 252
7.3.3.3 GO analysis 254
7.3.3.4 Pathway analysis 254
7.3.4 Simulation Study 254
7.3.5 Results and Discussion 255
7.3.5.1 Simulation data 255
7.3.5.2 Affy947 expression dataset 255
7.3.6 Conclusions 257
7.4 Portfolio Estimation for Spectral Density of Categorical Time Series Data 259
7.4.1 Introduction 259
7.4.2 Method 259
7.4.2.1 Spectral Envelope 259
7.4.2.2 Diversification analysis 260
7.4.2.3 An extension of SpecEnv to the mean-diversification efficient
frontier 260
7.4.3 Data 261
7.4.3.1 Simulation data 261
7.4.3.2 DNA sequence data 261
7.4.4 Results and Discussion 262
7.4.4.1 Simulation study 262
Vili
CONTENTS
7,4.4.2 DNA sequence for the BNRF1 genes 263
7.4.5 Conclusions 268
7.5 Application to Real-Value Time Series Data for Corticomuscular Functional
Coupling for SpecEnv and the Portfolio Study 270
7.5.1 Introduction 270
7.5.2 Method 274
7.5.3 Results and Discussion 274
8 Theoretical Foundations and Technicalities 283
8.1 Limit Theorems for Stochastic Processes 283
8.2 Statistical Asymptotic Theory 287
8.3 Statistical Optimal Theory 293
8.4 Statistical Model Selection 300
8.5 Efficient Estimation for Portfolios 312
8.5.1 Traditional mean variance portfolio estimators 313
8.5.2 Efficient mean variance portfolio estimators 315
8.5.3 Appendix 324
8.6 Shrinkage Estimation 331
8.7 Shrinkage Interpolation for Stationary Processes 342
Bibliography 349
Author Index 365
Subject Index
371
|
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| language | English |
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| spelling | Taniguchi, Masanobu Verfasser aut Statistical portfolio estimation Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, and Takashi Yamashita Boca Raton, Fla. CRC Press [2017] x, 377 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Mathematisches Modell Finance Statistical methods Finance Mathematical models Portfolio management Mathematical models Portfolio Selection (DE-588)4046834-3 gnd rswk-swf Schätzung (DE-588)4193791-0 gnd rswk-swf Risikomanagement (DE-588)4121590-4 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Portfolio Selection (DE-588)4046834-3 s Risikomanagement (DE-588)4121590-4 s Statistische Analyse (DE-588)4116599-8 s Schätzung (DE-588)4193791-0 s b DE-604 Shiraishi, Hiroshi 1974- Verfasser (DE-588)132644274 aut Hirukawa, Junichi Verfasser aut Solvang, Hiroko Kato Verfasser (DE-588)113985139X aut Yamashita, Takashi Verfasser aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029957623&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Taniguchi, Masanobu Shiraishi, Hiroshi 1974- Hirukawa, Junichi Solvang, Hiroko Kato Yamashita, Takashi Statistical portfolio estimation Mathematisches Modell Finance Statistical methods Finance Mathematical models Portfolio management Mathematical models Portfolio Selection (DE-588)4046834-3 gnd Schätzung (DE-588)4193791-0 gnd Risikomanagement (DE-588)4121590-4 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| subject_GND | (DE-588)4046834-3 (DE-588)4193791-0 (DE-588)4121590-4 (DE-588)4116599-8 |
| title | Statistical portfolio estimation |
| title_auth | Statistical portfolio estimation |
| title_exact_search | Statistical portfolio estimation |
| title_full | Statistical portfolio estimation Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, and Takashi Yamashita |
| title_fullStr | Statistical portfolio estimation Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, and Takashi Yamashita |
| title_full_unstemmed | Statistical portfolio estimation Masanobu Taniguchi, Hiroshi Shiraishi, Junichi Hirukawa, Hiroko Kato Solvang, and Takashi Yamashita |
| title_short | Statistical portfolio estimation |
| title_sort | statistical portfolio estimation |
| topic | Mathematisches Modell Finance Statistical methods Finance Mathematical models Portfolio management Mathematical models Portfolio Selection (DE-588)4046834-3 gnd Schätzung (DE-588)4193791-0 gnd Risikomanagement (DE-588)4121590-4 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| topic_facet | Mathematisches Modell Finance Statistical methods Finance Mathematical models Portfolio management Mathematical models Portfolio Selection Schätzung Risikomanagement Statistische Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029957623&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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