Quantitative Portfolio Management: With Applications in Python
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2020
|
| Schriftenreihe: | Springer Texts in Business and Economics Ser
|
| Schlagworte: | |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (212 pages) |
| ISBN: | 9783030377403 |
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| 505 | 8 | |a Intro -- Preface -- Contents -- 1 Returns and the Gaussian Hypothesis -- 1.1 Measure of the Performance -- 1.1.1 Return -- 1.1.2 Rate of Return -- 1.2 Probabilistic and Empirical Definitions -- 1.3 Goodness of Fit Tests -- 1.3.1 Example: Testing the Normality of the Returns of the DAX 30 -- 1.4 Further Statistical Results -- 1.4.1 Convergence of the Density Function Estimate -- 1.4.2 Tests Based on Cumulative Distribution Function Estimates -- 1.4.3 Tests Based on Order Statistics -- 1.4.4 Parameter Estimation and Confidence Intervals -- 1.5 Market Data with Python -- 1.5.1 Data Extraction for the DAX 30 -- 1.5.2 Statistical Analysis for the DAX 30 -- A Few References -- 2 Utility Functions and the Theory of Choice -- 2.1 Utility Functions and Preferred Investments -- 2.1.1 Risk Appetite and Concavity -- 2.2 Gaussian Laws and Mean-Variance Implications -- 2.3 Efficient Investment Strategies -- A Few References -- 3 The Markowitz Framework -- 3.1 Investment and Self-Financing Portfolios -- 3.1.1 Notations and Definitions -- 3.1.2 Representations of the Portfolios -- 3.1.3 Return of a Portfolio -- 3.2 Absence of Arbitrage Opportunities -- 3.2.1 Analysis of the Variance-Covariance Matrix -- 3.2.2 The Correlation Matrix -- 3.3 Multidimensional Estimations -- 3.3.1 Wishart, Hotelling's T2 and Fisher-Snedecor Distributions -- 3.3.2 Mean Vector and Variance-Covariance Matrix Estimates -- 3.3.3 Confidence Domain and Statistical Tests -- 3.4 Maket Data with Python -- A Few References -- 4 Markowitz Without a Risk-Free Asset -- 4.1 The Optimisation Problem -- 4.2 The Geometric Nature of the Set F(σ,m) -- 4.3 The Two Fund Theorem -- 4.3.1 Example with Two Assets: Importance of the Correlation -- 4.4 Alternative Parametrisation of F(σ, m) and Conclusion -- A Few References -- 5 Markowitz with a Risk-Free Asset -- 5.1 The Optimisation Problem | |
| 505 | 8 | |a 5.2 Capital Market Line and Limit Cone C(σ, m) -- 5.2.1 The Market Portfolio -- 5.2.2 The Tangent Portfolio -- 5.2.3 More Geometric Properties -- 5.3 The Security Market Line -- 5.3.1 The Security Market Line and ''Arbitrage'' Detections -- 5.4 Market Data with Python -- 5.4.1 The Frontier and Capital Market Line for the DAX 30 Components -- 5.4.2 Adding Additional Constraints -- 5.5 Stability of the Solutions -- 5.5.1 Stabilisation by Correlation Adjustment -- 5.6 The Bayesian Approach -- 5.6.1 Jeffrey's Prior μ0 on M and -- 5.6.2 Gaussian Prior μ0 on M -- 5.6.3 The Black-Litterman Model -- A Few References -- 6 Performance and Diversification Indicators -- 6.1 The Sharpe Ratio -- 6.2 The Jensen Index -- 6.3 The Treynor Index -- 6.4 Other Risk/Return Indicators -- 6.5 The Diversification Ratio -- A Few References -- 7 Risk Measures and Capital Allocation -- 7.1 Definition of a Risk Measure -- 7.2 Risk Measure in the Markowitz Framework -- 7.2.1 The Markowitz Risk Measure -- 7.2.2 Value at Risk -- 7.2.3 Expected Shortfall -- 7.3 Euler's Formula and Capital Allocation -- 7.3.1 Example of Risk Measure and Capital Allocation -- 7.4 Return on Risk-Adjusted Capital -- 7.4.1 Maximising the RORAC -- 7.4.2 Capital Allocation for a Positive Homogeneous Risk Measure -- 7.4.3 Example: Euler Allocation -- 7.4.4 Example: RORAC for Optimal Portfolios -- 7.4.5 Calculation of a Portfolio VaR, from Observed Asset Prices -- 7.4.6 Example: Boostrap Historical Simulation for a Portfolio VaR -- A Few References -- 8 Factor Models -- 8.1 Definitions and Notations -- 8.1.1 The Tangent Portfolio as a Factor -- 8.1.2 Endogenous and Exogenous Factors -- 8.1.3 Standard Form for a Factor Model -- 8.2 Identifying the Coefficients When the Factors Are Known -- 8.2.1 Regression on the Factors -- 8.3 Example of a Factor Model -- 8.4 APT Models -- 8.4.1 Example of an APT Model | |
| 505 | 8 | |a 8.4.2 Further Remarks -- 8.4.3 Standard Form for an APT Model -- 8.5 Alternative Definition of an APT Model -- 8.5.1 Estimation of the Risk Premia in an APT Model -- A Few References -- 9 Identification of the Factors -- 9.1 Total Inertia and Trace of the Variance-Covariance Matrix -- 9.2 Total Inertia of the Projection -- 9.3 Principal Component Analysis and Factors -- 9.3.1 PCA of the Matrix of Variance-Covariance -- 9.3.2 PCA of the Correlation Matrix -- 9.4 Principal Components and Eigenvalues Visualisation -- 9.5 Python: Application to the DAX 30 Components -- 9.5.1 Factors Explaining the Variance for the DAX 30 Components -- 9.5.2 Explanation of the Factors for the DAX 30 Components -- A Few References -- 10 Exercises and Problems -- 10.1 Midterm Exam, November 2015 -- Master M1: Mido 2nd November 2015 (Midterm Exam: Portfolio Management) -- 10.1.1 Solutions: Midterm Exam, November 2015 -- Master M1: Mido 2015-2016 (Midterm Exam: Portfolio Management) -- 10.2 Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management: Time 1h 30min) -- 10.2.1 Solutions: Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management) -- 10.3 Midterm Exam, November 2016 -- Master M1: Mido 3rd November 2016 (Exam: Portfolio Management: Time 2h) -- 10.3.1 Solutions: Midterm Exam, November 2016 -- 10.4 Exam, January 2017 -- Master M1: Mido 11th January 2017 (Exam: Portfolio Management: Time 2h) -- 10.4.1 Solutions: Exam, January 2017 -- 10.5 Midterm Exam, November 2017 -- Master M1: Mido 2nd November 2017 (Midterm Exam: Portfolio Management: Time 2h) -- 10.5.1 Solutions: Midterm Exam, November 2017 -- 10.6 Exam, January 2018 -- Master M1: Mido 15th January 2018 (Exam: Portfolio Management: Time 2h) -- 10.6.1 Solutions: Exam, January 2018 -- 10.7 Midterm Exam, October 2018 | |
| 505 | 8 | |a Master M1: Mido 29th October 2018 (Midterm Exam: Portfolio Management: Time 2h) -- 10.7.1 Solutions: Midterm Exam October 2018 -- A The Lagrangian -- A.1 Main Results -- A.1.1 Solution of the Markowitz Problem -- B Parametrisations -- B.1 Confidence Domain for an Estimator of M -- B.2 Confidence Domain for an Observation Ri -- Bibliography -- Index | |
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Datensatz im Suchindex
| _version_ | 1804184004102979584 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Brugière, Pierre |
| author_facet | Brugière, Pierre |
| author_role | aut |
| author_sort | Brugière, Pierre |
| author_variant | p b pb |
| building | Verbundindex |
| bvnumber | BV048222598 |
| classification_rvk | QK 810 SK 980 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Contents -- 1 Returns and the Gaussian Hypothesis -- 1.1 Measure of the Performance -- 1.1.1 Return -- 1.1.2 Rate of Return -- 1.2 Probabilistic and Empirical Definitions -- 1.3 Goodness of Fit Tests -- 1.3.1 Example: Testing the Normality of the Returns of the DAX 30 -- 1.4 Further Statistical Results -- 1.4.1 Convergence of the Density Function Estimate -- 1.4.2 Tests Based on Cumulative Distribution Function Estimates -- 1.4.3 Tests Based on Order Statistics -- 1.4.4 Parameter Estimation and Confidence Intervals -- 1.5 Market Data with Python -- 1.5.1 Data Extraction for the DAX 30 -- 1.5.2 Statistical Analysis for the DAX 30 -- A Few References -- 2 Utility Functions and the Theory of Choice -- 2.1 Utility Functions and Preferred Investments -- 2.1.1 Risk Appetite and Concavity -- 2.2 Gaussian Laws and Mean-Variance Implications -- 2.3 Efficient Investment Strategies -- A Few References -- 3 The Markowitz Framework -- 3.1 Investment and Self-Financing Portfolios -- 3.1.1 Notations and Definitions -- 3.1.2 Representations of the Portfolios -- 3.1.3 Return of a Portfolio -- 3.2 Absence of Arbitrage Opportunities -- 3.2.1 Analysis of the Variance-Covariance Matrix -- 3.2.2 The Correlation Matrix -- 3.3 Multidimensional Estimations -- 3.3.1 Wishart, Hotelling's T2 and Fisher-Snedecor Distributions -- 3.3.2 Mean Vector and Variance-Covariance Matrix Estimates -- 3.3.3 Confidence Domain and Statistical Tests -- 3.4 Maket Data with Python -- A Few References -- 4 Markowitz Without a Risk-Free Asset -- 4.1 The Optimisation Problem -- 4.2 The Geometric Nature of the Set F(σ,m) -- 4.3 The Two Fund Theorem -- 4.3.1 Example with Two Assets: Importance of the Correlation -- 4.4 Alternative Parametrisation of F(σ, m) and Conclusion -- A Few References -- 5 Markowitz with a Risk-Free Asset -- 5.1 The Optimisation Problem 5.2 Capital Market Line and Limit Cone C(σ, m) -- 5.2.1 The Market Portfolio -- 5.2.2 The Tangent Portfolio -- 5.2.3 More Geometric Properties -- 5.3 The Security Market Line -- 5.3.1 The Security Market Line and ''Arbitrage'' Detections -- 5.4 Market Data with Python -- 5.4.1 The Frontier and Capital Market Line for the DAX 30 Components -- 5.4.2 Adding Additional Constraints -- 5.5 Stability of the Solutions -- 5.5.1 Stabilisation by Correlation Adjustment -- 5.6 The Bayesian Approach -- 5.6.1 Jeffrey's Prior μ0 on M and -- 5.6.2 Gaussian Prior μ0 on M -- 5.6.3 The Black-Litterman Model -- A Few References -- 6 Performance and Diversification Indicators -- 6.1 The Sharpe Ratio -- 6.2 The Jensen Index -- 6.3 The Treynor Index -- 6.4 Other Risk/Return Indicators -- 6.5 The Diversification Ratio -- A Few References -- 7 Risk Measures and Capital Allocation -- 7.1 Definition of a Risk Measure -- 7.2 Risk Measure in the Markowitz Framework -- 7.2.1 The Markowitz Risk Measure -- 7.2.2 Value at Risk -- 7.2.3 Expected Shortfall -- 7.3 Euler's Formula and Capital Allocation -- 7.3.1 Example of Risk Measure and Capital Allocation -- 7.4 Return on Risk-Adjusted Capital -- 7.4.1 Maximising the RORAC -- 7.4.2 Capital Allocation for a Positive Homogeneous Risk Measure -- 7.4.3 Example: Euler Allocation -- 7.4.4 Example: RORAC for Optimal Portfolios -- 7.4.5 Calculation of a Portfolio VaR, from Observed Asset Prices -- 7.4.6 Example: Boostrap Historical Simulation for a Portfolio VaR -- A Few References -- 8 Factor Models -- 8.1 Definitions and Notations -- 8.1.1 The Tangent Portfolio as a Factor -- 8.1.2 Endogenous and Exogenous Factors -- 8.1.3 Standard Form for a Factor Model -- 8.2 Identifying the Coefficients When the Factors Are Known -- 8.2.1 Regression on the Factors -- 8.3 Example of a Factor Model -- 8.4 APT Models -- 8.4.1 Example of an APT Model 8.4.2 Further Remarks -- 8.4.3 Standard Form for an APT Model -- 8.5 Alternative Definition of an APT Model -- 8.5.1 Estimation of the Risk Premia in an APT Model -- A Few References -- 9 Identification of the Factors -- 9.1 Total Inertia and Trace of the Variance-Covariance Matrix -- 9.2 Total Inertia of the Projection -- 9.3 Principal Component Analysis and Factors -- 9.3.1 PCA of the Matrix of Variance-Covariance -- 9.3.2 PCA of the Correlation Matrix -- 9.4 Principal Components and Eigenvalues Visualisation -- 9.5 Python: Application to the DAX 30 Components -- 9.5.1 Factors Explaining the Variance for the DAX 30 Components -- 9.5.2 Explanation of the Factors for the DAX 30 Components -- A Few References -- 10 Exercises and Problems -- 10.1 Midterm Exam, November 2015 -- Master M1: Mido 2nd November 2015 (Midterm Exam: Portfolio Management) -- 10.1.1 Solutions: Midterm Exam, November 2015 -- Master M1: Mido 2015-2016 (Midterm Exam: Portfolio Management) -- 10.2 Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management: Time 1h 30min) -- 10.2.1 Solutions: Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management) -- 10.3 Midterm Exam, November 2016 -- Master M1: Mido 3rd November 2016 (Exam: Portfolio Management: Time 2h) -- 10.3.1 Solutions: Midterm Exam, November 2016 -- 10.4 Exam, January 2017 -- Master M1: Mido 11th January 2017 (Exam: Portfolio Management: Time 2h) -- 10.4.1 Solutions: Exam, January 2017 -- 10.5 Midterm Exam, November 2017 -- Master M1: Mido 2nd November 2017 (Midterm Exam: Portfolio Management: Time 2h) -- 10.5.1 Solutions: Midterm Exam, November 2017 -- 10.6 Exam, January 2018 -- Master M1: Mido 15th January 2018 (Exam: Portfolio Management: Time 2h) -- 10.6.1 Solutions: Exam, January 2018 -- 10.7 Midterm Exam, October 2018 Master M1: Mido 29th October 2018 (Midterm Exam: Portfolio Management: Time 2h) -- 10.7.1 Solutions: Midterm Exam October 2018 -- A The Lagrangian -- A.1 Main Results -- A.1.1 Solution of the Markowitz Problem -- B Parametrisations -- B.1 Confidence Domain for an Estimator of M -- B.2 Confidence Domain for an Observation Ri -- Bibliography -- Index |
| ctrlnum | (ZDB-30-PQE)EBC6147797 (ZDB-30-PAD)EBC6147797 (ZDB-89-EBL)EBL6147797 (OCoLC)1148885924 (DE-599)BVBBV048222598 |
| dewey-full | 332.6 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.6 |
| dewey-search | 332.6 |
| dewey-sort | 3332.6 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV048222598 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:37Z |
| indexdate | 2024-07-10T09:32:26Z |
| institution | BVB |
| isbn | 9783030377403 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033603331 |
| oclc_num | 1148885924 |
| open_access_boolean | |
| physical | 1 Online-Ressource (212 pages) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Springer Texts in Business and Economics Ser |
| spelling | Brugière, Pierre Verfasser aut Quantitative Portfolio Management With Applications in Python Cham Springer International Publishing AG 2020 ©2020 1 Online-Ressource (212 pages) txt rdacontent c rdamedia cr rdacarrier Springer Texts in Business and Economics Ser Description based on publisher supplied metadata and other sources Intro -- Preface -- Contents -- 1 Returns and the Gaussian Hypothesis -- 1.1 Measure of the Performance -- 1.1.1 Return -- 1.1.2 Rate of Return -- 1.2 Probabilistic and Empirical Definitions -- 1.3 Goodness of Fit Tests -- 1.3.1 Example: Testing the Normality of the Returns of the DAX 30 -- 1.4 Further Statistical Results -- 1.4.1 Convergence of the Density Function Estimate -- 1.4.2 Tests Based on Cumulative Distribution Function Estimates -- 1.4.3 Tests Based on Order Statistics -- 1.4.4 Parameter Estimation and Confidence Intervals -- 1.5 Market Data with Python -- 1.5.1 Data Extraction for the DAX 30 -- 1.5.2 Statistical Analysis for the DAX 30 -- A Few References -- 2 Utility Functions and the Theory of Choice -- 2.1 Utility Functions and Preferred Investments -- 2.1.1 Risk Appetite and Concavity -- 2.2 Gaussian Laws and Mean-Variance Implications -- 2.3 Efficient Investment Strategies -- A Few References -- 3 The Markowitz Framework -- 3.1 Investment and Self-Financing Portfolios -- 3.1.1 Notations and Definitions -- 3.1.2 Representations of the Portfolios -- 3.1.3 Return of a Portfolio -- 3.2 Absence of Arbitrage Opportunities -- 3.2.1 Analysis of the Variance-Covariance Matrix -- 3.2.2 The Correlation Matrix -- 3.3 Multidimensional Estimations -- 3.3.1 Wishart, Hotelling's T2 and Fisher-Snedecor Distributions -- 3.3.2 Mean Vector and Variance-Covariance Matrix Estimates -- 3.3.3 Confidence Domain and Statistical Tests -- 3.4 Maket Data with Python -- A Few References -- 4 Markowitz Without a Risk-Free Asset -- 4.1 The Optimisation Problem -- 4.2 The Geometric Nature of the Set F(σ,m) -- 4.3 The Two Fund Theorem -- 4.3.1 Example with Two Assets: Importance of the Correlation -- 4.4 Alternative Parametrisation of F(σ, m) and Conclusion -- A Few References -- 5 Markowitz with a Risk-Free Asset -- 5.1 The Optimisation Problem 5.2 Capital Market Line and Limit Cone C(σ, m) -- 5.2.1 The Market Portfolio -- 5.2.2 The Tangent Portfolio -- 5.2.3 More Geometric Properties -- 5.3 The Security Market Line -- 5.3.1 The Security Market Line and ''Arbitrage'' Detections -- 5.4 Market Data with Python -- 5.4.1 The Frontier and Capital Market Line for the DAX 30 Components -- 5.4.2 Adding Additional Constraints -- 5.5 Stability of the Solutions -- 5.5.1 Stabilisation by Correlation Adjustment -- 5.6 The Bayesian Approach -- 5.6.1 Jeffrey's Prior μ0 on M and -- 5.6.2 Gaussian Prior μ0 on M -- 5.6.3 The Black-Litterman Model -- A Few References -- 6 Performance and Diversification Indicators -- 6.1 The Sharpe Ratio -- 6.2 The Jensen Index -- 6.3 The Treynor Index -- 6.4 Other Risk/Return Indicators -- 6.5 The Diversification Ratio -- A Few References -- 7 Risk Measures and Capital Allocation -- 7.1 Definition of a Risk Measure -- 7.2 Risk Measure in the Markowitz Framework -- 7.2.1 The Markowitz Risk Measure -- 7.2.2 Value at Risk -- 7.2.3 Expected Shortfall -- 7.3 Euler's Formula and Capital Allocation -- 7.3.1 Example of Risk Measure and Capital Allocation -- 7.4 Return on Risk-Adjusted Capital -- 7.4.1 Maximising the RORAC -- 7.4.2 Capital Allocation for a Positive Homogeneous Risk Measure -- 7.4.3 Example: Euler Allocation -- 7.4.4 Example: RORAC for Optimal Portfolios -- 7.4.5 Calculation of a Portfolio VaR, from Observed Asset Prices -- 7.4.6 Example: Boostrap Historical Simulation for a Portfolio VaR -- A Few References -- 8 Factor Models -- 8.1 Definitions and Notations -- 8.1.1 The Tangent Portfolio as a Factor -- 8.1.2 Endogenous and Exogenous Factors -- 8.1.3 Standard Form for a Factor Model -- 8.2 Identifying the Coefficients When the Factors Are Known -- 8.2.1 Regression on the Factors -- 8.3 Example of a Factor Model -- 8.4 APT Models -- 8.4.1 Example of an APT Model 8.4.2 Further Remarks -- 8.4.3 Standard Form for an APT Model -- 8.5 Alternative Definition of an APT Model -- 8.5.1 Estimation of the Risk Premia in an APT Model -- A Few References -- 9 Identification of the Factors -- 9.1 Total Inertia and Trace of the Variance-Covariance Matrix -- 9.2 Total Inertia of the Projection -- 9.3 Principal Component Analysis and Factors -- 9.3.1 PCA of the Matrix of Variance-Covariance -- 9.3.2 PCA of the Correlation Matrix -- 9.4 Principal Components and Eigenvalues Visualisation -- 9.5 Python: Application to the DAX 30 Components -- 9.5.1 Factors Explaining the Variance for the DAX 30 Components -- 9.5.2 Explanation of the Factors for the DAX 30 Components -- A Few References -- 10 Exercises and Problems -- 10.1 Midterm Exam, November 2015 -- Master M1: Mido 2nd November 2015 (Midterm Exam: Portfolio Management) -- 10.1.1 Solutions: Midterm Exam, November 2015 -- Master M1: Mido 2015-2016 (Midterm Exam: Portfolio Management) -- 10.2 Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management: Time 1h 30min) -- 10.2.1 Solutions: Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management) -- 10.3 Midterm Exam, November 2016 -- Master M1: Mido 3rd November 2016 (Exam: Portfolio Management: Time 2h) -- 10.3.1 Solutions: Midterm Exam, November 2016 -- 10.4 Exam, January 2017 -- Master M1: Mido 11th January 2017 (Exam: Portfolio Management: Time 2h) -- 10.4.1 Solutions: Exam, January 2017 -- 10.5 Midterm Exam, November 2017 -- Master M1: Mido 2nd November 2017 (Midterm Exam: Portfolio Management: Time 2h) -- 10.5.1 Solutions: Midterm Exam, November 2017 -- 10.6 Exam, January 2018 -- Master M1: Mido 15th January 2018 (Exam: Portfolio Management: Time 2h) -- 10.6.1 Solutions: Exam, January 2018 -- 10.7 Midterm Exam, October 2018 Master M1: Mido 29th October 2018 (Midterm Exam: Portfolio Management: Time 2h) -- 10.7.1 Solutions: Midterm Exam October 2018 -- A The Lagrangian -- A.1 Main Results -- A.1.1 Solution of the Markowitz Problem -- B Parametrisations -- B.1 Confidence Domain for an Estimator of M -- B.2 Confidence Domain for an Observation Ri -- Bibliography -- Index Portfolio management Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Portfoliomanagement (DE-588)4115601-8 gnd rswk-swf Kapitalanlage (DE-588)4073213-7 gnd rswk-swf Quantitative Methode (DE-588)4232139-6 gnd rswk-swf Portfoliomanagement (DE-588)4115601-8 s Quantitative Methode (DE-588)4232139-6 s Kapitalanlage (DE-588)4073213-7 s Finanzmathematik (DE-588)4017195-4 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Erscheint auch als Druck-Ausgabe Brugière, Pierre Quantitative Portfolio Management Cham : Springer International Publishing AG,c2020 9783030377397 |
| spellingShingle | Brugière, Pierre Quantitative Portfolio Management With Applications in Python Intro -- Preface -- Contents -- 1 Returns and the Gaussian Hypothesis -- 1.1 Measure of the Performance -- 1.1.1 Return -- 1.1.2 Rate of Return -- 1.2 Probabilistic and Empirical Definitions -- 1.3 Goodness of Fit Tests -- 1.3.1 Example: Testing the Normality of the Returns of the DAX 30 -- 1.4 Further Statistical Results -- 1.4.1 Convergence of the Density Function Estimate -- 1.4.2 Tests Based on Cumulative Distribution Function Estimates -- 1.4.3 Tests Based on Order Statistics -- 1.4.4 Parameter Estimation and Confidence Intervals -- 1.5 Market Data with Python -- 1.5.1 Data Extraction for the DAX 30 -- 1.5.2 Statistical Analysis for the DAX 30 -- A Few References -- 2 Utility Functions and the Theory of Choice -- 2.1 Utility Functions and Preferred Investments -- 2.1.1 Risk Appetite and Concavity -- 2.2 Gaussian Laws and Mean-Variance Implications -- 2.3 Efficient Investment Strategies -- A Few References -- 3 The Markowitz Framework -- 3.1 Investment and Self-Financing Portfolios -- 3.1.1 Notations and Definitions -- 3.1.2 Representations of the Portfolios -- 3.1.3 Return of a Portfolio -- 3.2 Absence of Arbitrage Opportunities -- 3.2.1 Analysis of the Variance-Covariance Matrix -- 3.2.2 The Correlation Matrix -- 3.3 Multidimensional Estimations -- 3.3.1 Wishart, Hotelling's T2 and Fisher-Snedecor Distributions -- 3.3.2 Mean Vector and Variance-Covariance Matrix Estimates -- 3.3.3 Confidence Domain and Statistical Tests -- 3.4 Maket Data with Python -- A Few References -- 4 Markowitz Without a Risk-Free Asset -- 4.1 The Optimisation Problem -- 4.2 The Geometric Nature of the Set F(σ,m) -- 4.3 The Two Fund Theorem -- 4.3.1 Example with Two Assets: Importance of the Correlation -- 4.4 Alternative Parametrisation of F(σ, m) and Conclusion -- A Few References -- 5 Markowitz with a Risk-Free Asset -- 5.1 The Optimisation Problem 5.2 Capital Market Line and Limit Cone C(σ, m) -- 5.2.1 The Market Portfolio -- 5.2.2 The Tangent Portfolio -- 5.2.3 More Geometric Properties -- 5.3 The Security Market Line -- 5.3.1 The Security Market Line and ''Arbitrage'' Detections -- 5.4 Market Data with Python -- 5.4.1 The Frontier and Capital Market Line for the DAX 30 Components -- 5.4.2 Adding Additional Constraints -- 5.5 Stability of the Solutions -- 5.5.1 Stabilisation by Correlation Adjustment -- 5.6 The Bayesian Approach -- 5.6.1 Jeffrey's Prior μ0 on M and -- 5.6.2 Gaussian Prior μ0 on M -- 5.6.3 The Black-Litterman Model -- A Few References -- 6 Performance and Diversification Indicators -- 6.1 The Sharpe Ratio -- 6.2 The Jensen Index -- 6.3 The Treynor Index -- 6.4 Other Risk/Return Indicators -- 6.5 The Diversification Ratio -- A Few References -- 7 Risk Measures and Capital Allocation -- 7.1 Definition of a Risk Measure -- 7.2 Risk Measure in the Markowitz Framework -- 7.2.1 The Markowitz Risk Measure -- 7.2.2 Value at Risk -- 7.2.3 Expected Shortfall -- 7.3 Euler's Formula and Capital Allocation -- 7.3.1 Example of Risk Measure and Capital Allocation -- 7.4 Return on Risk-Adjusted Capital -- 7.4.1 Maximising the RORAC -- 7.4.2 Capital Allocation for a Positive Homogeneous Risk Measure -- 7.4.3 Example: Euler Allocation -- 7.4.4 Example: RORAC for Optimal Portfolios -- 7.4.5 Calculation of a Portfolio VaR, from Observed Asset Prices -- 7.4.6 Example: Boostrap Historical Simulation for a Portfolio VaR -- A Few References -- 8 Factor Models -- 8.1 Definitions and Notations -- 8.1.1 The Tangent Portfolio as a Factor -- 8.1.2 Endogenous and Exogenous Factors -- 8.1.3 Standard Form for a Factor Model -- 8.2 Identifying the Coefficients When the Factors Are Known -- 8.2.1 Regression on the Factors -- 8.3 Example of a Factor Model -- 8.4 APT Models -- 8.4.1 Example of an APT Model 8.4.2 Further Remarks -- 8.4.3 Standard Form for an APT Model -- 8.5 Alternative Definition of an APT Model -- 8.5.1 Estimation of the Risk Premia in an APT Model -- A Few References -- 9 Identification of the Factors -- 9.1 Total Inertia and Trace of the Variance-Covariance Matrix -- 9.2 Total Inertia of the Projection -- 9.3 Principal Component Analysis and Factors -- 9.3.1 PCA of the Matrix of Variance-Covariance -- 9.3.2 PCA of the Correlation Matrix -- 9.4 Principal Components and Eigenvalues Visualisation -- 9.5 Python: Application to the DAX 30 Components -- 9.5.1 Factors Explaining the Variance for the DAX 30 Components -- 9.5.2 Explanation of the Factors for the DAX 30 Components -- A Few References -- 10 Exercises and Problems -- 10.1 Midterm Exam, November 2015 -- Master M1: Mido 2nd November 2015 (Midterm Exam: Portfolio Management) -- 10.1.1 Solutions: Midterm Exam, November 2015 -- Master M1: Mido 2015-2016 (Midterm Exam: Portfolio Management) -- 10.2 Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management: Time 1h 30min) -- 10.2.1 Solutions: Exam, January 2016 -- Master M1: Mido 5th January 2016 (Exam: Portfolio Management) -- 10.3 Midterm Exam, November 2016 -- Master M1: Mido 3rd November 2016 (Exam: Portfolio Management: Time 2h) -- 10.3.1 Solutions: Midterm Exam, November 2016 -- 10.4 Exam, January 2017 -- Master M1: Mido 11th January 2017 (Exam: Portfolio Management: Time 2h) -- 10.4.1 Solutions: Exam, January 2017 -- 10.5 Midterm Exam, November 2017 -- Master M1: Mido 2nd November 2017 (Midterm Exam: Portfolio Management: Time 2h) -- 10.5.1 Solutions: Midterm Exam, November 2017 -- 10.6 Exam, January 2018 -- Master M1: Mido 15th January 2018 (Exam: Portfolio Management: Time 2h) -- 10.6.1 Solutions: Exam, January 2018 -- 10.7 Midterm Exam, October 2018 Master M1: Mido 29th October 2018 (Midterm Exam: Portfolio Management: Time 2h) -- 10.7.1 Solutions: Midterm Exam October 2018 -- A The Lagrangian -- A.1 Main Results -- A.1.1 Solution of the Markowitz Problem -- B Parametrisations -- B.1 Confidence Domain for an Estimator of M -- B.2 Confidence Domain for an Observation Ri -- Bibliography -- Index Portfolio management Python Programmiersprache (DE-588)4434275-5 gnd Finanzmathematik (DE-588)4017195-4 gnd Portfoliomanagement (DE-588)4115601-8 gnd Kapitalanlage (DE-588)4073213-7 gnd Quantitative Methode (DE-588)4232139-6 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4017195-4 (DE-588)4115601-8 (DE-588)4073213-7 (DE-588)4232139-6 |
| title | Quantitative Portfolio Management With Applications in Python |
| title_auth | Quantitative Portfolio Management With Applications in Python |
| title_exact_search | Quantitative Portfolio Management With Applications in Python |
| title_exact_search_txtP | Quantitative Portfolio Management With Applications in Python |
| title_full | Quantitative Portfolio Management With Applications in Python |
| title_fullStr | Quantitative Portfolio Management With Applications in Python |
| title_full_unstemmed | Quantitative Portfolio Management With Applications in Python |
| title_short | Quantitative Portfolio Management |
| title_sort | quantitative portfolio management with applications in python |
| title_sub | With Applications in Python |
| topic | Portfolio management Python Programmiersprache (DE-588)4434275-5 gnd Finanzmathematik (DE-588)4017195-4 gnd Portfoliomanagement (DE-588)4115601-8 gnd Kapitalanlage (DE-588)4073213-7 gnd Quantitative Methode (DE-588)4232139-6 gnd |
| topic_facet | Portfolio management Python Programmiersprache Finanzmathematik Portfoliomanagement Kapitalanlage Quantitative Methode |
| work_keys_str_mv | AT brugierepierre quantitativeportfoliomanagementwithapplicationsinpython |