Statistical portfolio estimation / Masanobu Taniguchi, [and four others].

Format:

Book

Author/Creator:

Taniguchi, Masanobu, author.

Language:

English

Subjects (All):

Finance--Statistical methods.

Finance.

Finance--Mathematical models.

Portfolio management--Mathematical models.

Portfolio management.

Physical Description:

x, 377 pages ; 27 cm

Place of Publication:

Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]

Contents:

2.1 Stochastic Processes and Limit Theorems p. 5

3 Portfolio Theory for Dependent Return Processes p. 19

3.1 Introduction to Portfolio Theory p. 19

3.1.1 Mean-Variance Portfolio p. 20

3.1.2 Capital Asset Pricing Model p. 23

3.1.3 Arbitrage Pricing Theory p. 27

3.1.4 Expected Utility Theory p. 28

3.1.5 Alternative Risk Measures p. 33

3.1.6 Copulas and Dependence p. 36

3.2 Statistical Estimation for Portfolios p. 41

3.2.1 Traditional Mean-Variance Portfolio Estimators p. 42

3.2.2 Pessimistic Portfolio p. 44

3.2.3 Shrinkage Estimators p. 46

3.2.4 Bayesian Estimation p. 48

3.2.5 Factor Models p. 52

3.2.5.1 Static factor models p. 52

3.2.5.2 Dynamic factor models p. 55

3.2.6 High-Dimensional Problems p. 58

3.2.6.1 The case of m/n → y ∈ (0, 1) p. 59

3.2.6.2 The case of m/n → y ∈ (1, ∞) p. 62

3.3 Simulation Results p. 65

3.3.1 Quasi-Maximum Likelihood Estimator p. 66

3.3.2 Efficient Frontier p. 68

3.3.3 Difference between the True Point and the Estimated Point p. 69

3.3.4 Inference of μρ p. 70

3.3.5 Inference of Coefficient p. 72

4 Multiperiod Problem for Portfolio Theory p. 75

4.1 Discrete Time Problem p. 76

4.1.1 Optimal Portfolio Weights p. 76

4.1.2 Consumption Investment p. 78

4.1.3 Simulation Approach for VAR(1) model p. 80

4.2 Continuous Time Problem p. 84

4.2.1 Optimal Consumption and Portfolio Weights p. 85

4.2.2.1 Generalized method of moments (GMM) p. 91

4.2.2.2 Threshold estimation method p. 95

4.3 Universal Portfolio p. 98

4.3.1 μ-Weigh ted Universal Portfolios p. 98

4.3.2 Universal Portfolios with Side Information p. 101

4.3.3 Successive Constant Rebalanced Portfolios p. 104

4.3.4 Universal Portfolios with Transaction Costs p. 106

5 Portfolio Estimation Based on Rank Statistics p. 113

5.1 Introduction to Rank-Based Statisticsrank p. 113

5.1.1 History of Ranks p. 113

5.1.1.1 Wilcoxon's signed rank and rank sum tests p. 113

5.1.1.2 Hodges-Lehmann and Chemoff-Savage p. 117

5.1.2 Maximal Invariantsmaximal invariant p. 124

5.1.2.1 Invariance of sample space, parameter space and tests p. 124

5.1.2.2 Most powerful invariant testmost powerful invariant test p. 125

5.1.3 Efficiencyefficiency of Rank-Based Statistics p. 126

5.1.3.1 Least favourableleast favourable density and most powerfulmost powerful test p. 126

5.1.3.2 Asymptotically most powerful rank test p. 129

5.1.4 U-Statistics for Stationary Processes p. 137

5.2 Semiparametrically Efficient Estimation in Time Series p. 139

5.2.1 Introduction to Rank-Based Theory in Time Series p. 139

5.2.1.1 Testing for randomness against ARM A alternatives p. 139

5.2.1.2 Testing an ARM A model against other ARM A alternatives p. 146

5.2.2 Tangent Spacetangent space p. 149

5.2.3 Introduction to Semiparametric Asymptotic Optimal Theory p. 155

5.2.4 Semiparametrically Efficient Estimation in Time Series, and Multivariate Cases p. 159

5.2.4.1 Rank-based optimal influence functions (univariate case) p. 159

5.2.4.2 Rank-based optimal estimation for elliptical residuals p. 164

5.3 Asymptotic Theory of Rank Order Statistics for ARCH Residual Empirical Processes p. 170

5.4 Independent Component Analysis p. 180

5.4.1 Introduction to Independent Component Analysis p. 180

5.4.1.1 The foregoing model for financial time series p. 180

5.4.1.2 ICA modeling for financial time series p. 187

5.4.1.3 ICA modeling in frequency domain for time series p. 191

5.5 Rank-Based Optimal Portfolio Estimation p. 202

5.5.1 Portfolio Estimation Based on Ranks for Independent Components p. 202

5.5.2 Portfolio Estimation Based on Ranks for Elliptical Residualselliptical residuals p. 204

6 Portfolio Estimation Influenced by Non-Gaussian Innovations and Exogenous Variables p. 207

6.1 Robust Portfolio Estimation under Skew-Normal Return Processes p. 207

6.2 Portfolio Estimators Depending on Higher-Order Cumulant Spectra p. 211

6.3 Portfolio Estimation under the Utility Function Depending on Exogenous Variables p. 215

6.4 Multi-Step Ahead Portfolio Estimation p. 221

6.5 Causality Analysis p. 224

6.6 Classificationclalsification by Quantile Regressionquantile regression p. 227

6.7 Portfolio Estimation under Causal Variables p. 229

7.1 Real Data Analysis for Portfolio Estimation p. 235

7.1.3.1 Model selection p. 237

7.1.3.2 Confidence region p. 238

7.1.3.3 Locally stationary estimation p. 239

7.2 Application for Pension Investment p. 243

7.3 Microarray Analysis Using Rank Order Statistics for ARCH Residual p. 248

7.3.3.1 The rank order statistic for the ARCH residual empirical process p. 252

7.3.3.2 Two-group comparison for microarray data p. 252

7.3.3.3 GO analysis p. 254

7.3.3.4 Pathway analysis p. 254

7.3.4 Simulation Study p. 254

7.3.5.1 Simulation data p. 255

7.3.5.2 Affy947 expression dataset p. 255

7.4 Portfolio Estimation for Spectral Density of Categorical Time Series Data p. 259

7.4.2.1 Spectral Envelope p. 259

7.4.2.2 Diversification analysis p. 260

7.4.2.3 An extension of SpecEnv to the mean-diversification efficient frontier p. 260

7.4.3.1 Simulation data p. 261

7.4.3.2 DNA sequence data p. 261

7.4.4.1 Simulation study p. 262

7.4.4.2 DNA sequence for the BNRFI genes p. 263

7.5 Application to Real-Value Time Series Data for Corticomuscular Functional Coupling for SpecEnv and the Portfolio Study p. 270

8 Theoretical Foundations and Technicalities p. 283

8.1 Limit Theorems for Stochastic Processes p. 283

8.2 Statistical Asymptotic Theory p. 287

8.3 Statistical Optimal Theory p. 293

8.4 Statistical Model Selection p. 300

8.5 Efficient Estimation for Portfolios p. 312

8.5.1 Traditional mean variance portfolio estimators p. 313

8.5.2 Efficient mean variance portfolio estimators p. 315

8.6 Shrinkage Estimation p. 331

8.7 Shrinkage Interpolation for Stationary Processes p. 342.

Notes:

Includes bibliographical references and indexes.

ISBN:

9781466505605

1466505605

OCLC:

992119845