Quantitative portfolio management: with applications in Python
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2020]
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| Schriftenreihe: | Springer texts in business and economics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 205 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030377397 |
| ISSN: | 2192-4333 |
Internformat
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Datensatz im Suchindex
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| adam_text | Cöllterits 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......................................................................................... 1 1 1 2 3 5 9 11 12 16 16 16 18 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......................................................................................... 19 19 20 23 24 25 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....................................................... 27 27 28 28 30 32 33 33 6 8 8 ix
Contents 3.3 4 5 Multidimensional Estimations........................................................... 3.3.1 Wishart, Hotelling’s Г 2 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.......................................................................................... 35 Markowitz Without a Risk-Free Asset..................................................... 4.1 The Optimisation Problem................................................................. 4.2 The Geometric Nature of the Set T{a,m) ..................................... 4.3 The Two Fund Theorem.................................................................... 4.3.1 Example with Two Assets: Importance of the Correlation................................................................. 4.4 Alternative Parametrisation of Τ(σ, m) and Conclusion............... A Few References.......................................................................................... 51 51 54 56 Markowitz with a Risk-Free Asset............................................................ 5.1 The Optimisation Problem................................................................. 5.2 Capital Market Line and Limit Cone C{a, 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 до on M and Σ........................................... 5.6.2 Gaussian Prior до on M...................................................... 5.6.3 The Black-Litterman Model.............................................. A Few References.......................................................................................... 61 62 63 65 68 69 69 71 73 35 39 44 45 49 56 57 59 73 77 81 82 83 86 88 91 93 6 Performance and Diversification Indicators................................................ 95 6.1 The Sharpe Ratio......................................................................... 95
6.2 The Jensen Index............................................................................... 96 6.3 The Treynor Index............................................................................. 97 6.4 Other Risk/Return Indicators............................................................ 98 6.5 The Diversification Ratio................................................................. 98 A Few References.......................................................................................... 101
Contents 7 x¡ 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........................................................................................ 103 103 105 105 107 108 110 Ill 112 112 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......................................................................................... 125 125 127 127 128 129 131 132 133 136 137 137 137 138 139 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 CorrelationMatrix........................................... 9.4 Principal Components and Eigenvalues Visualisation.................... 141 141 142 144 144 147 147 113 116 117 119 120 123
xii Contents 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..................................................................................... 148 Exercises and Problems............................................. Midterm Exam, November 2015 ................................................... 10.1.1 Solutions: Midterm Exam, November 2015.................... Exam, January 2016....................................................................... 10.2.1 Solutions: Exam, January 2016....................................... Midterm Exam, November 2016................................................... 10.3.1 Solutions: Midterm Exam, November 2016.................... Exam, January 2017....................................................................... 10.4.1 Solutions: Exam, January 2017....................................... Midterm Exam, November 2017................................................... 10.5.1 Solutions: Midterm Exam, November 2017.................... Exam, January 2018....................................................................... 10.6.1 Solutions: Exam, January 2018....................................... Midterm Exam, October 2018....................................................... 10.7.1 Solutions: Midterm Exam October
2018......................... 155 155 157 159 162 164 166 168 172 174 177 179 182 184 187 10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 149 150 154 A The Lagrangian....................................................................................... 191 A.l Main Results.................................................................................. 191 A. 1.1 Solution of the Markowitz Problem................................ 193 В Parametrisations...................................................................................... 195 B. 1 Confidence Domain for an Estimator of M.................................... 195 B.2 Confidence Domain for an Observation R¡ ................................. 196 Bibliography................................................................................................... 199 Index................................................................................................................ 203
|
| adam_txt |
Cöllterits 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. 1 1 1 2 3 5 9 11 12 16 16 16 18 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. 19 19 20 23 24 25 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. 27 27 28 28 30 32 33 33 6 8 8 ix
Contents 3.3 4 5 Multidimensional Estimations. 3.3.1 Wishart, Hotelling’s Г 2 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. 35 Markowitz Without a Risk-Free Asset. 4.1 The Optimisation Problem. 4.2 The Geometric Nature of the Set T{a,m) . 4.3 The Two Fund Theorem. 4.3.1 Example with Two Assets: Importance of the Correlation. 4.4 Alternative Parametrisation of Τ(σ, m) and Conclusion. A Few References. 51 51 54 56 Markowitz with a Risk-Free Asset. 5.1 The Optimisation Problem. 5.2 Capital Market Line and Limit Cone C{a, 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 до on M and Σ. 5.6.2 Gaussian Prior до on M. 5.6.3 The Black-Litterman Model. A Few References. 61 62 63 65 68 69 69 71 73 35 39 44 45 49 56 57 59 73 77 81 82 83 86 88 91 93 6 Performance and Diversification Indicators. 95 6.1 The Sharpe Ratio. 95
6.2 The Jensen Index. 96 6.3 The Treynor Index. 97 6.4 Other Risk/Return Indicators. 98 6.5 The Diversification Ratio. 98 A Few References. 101
Contents 7 x¡ 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. 103 103 105 105 107 108 110 Ill 112 112 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. 125 125 127 127 128 129 131 132 133 136 137 137 137 138 139 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 CorrelationMatrix. 9.4 Principal Components and Eigenvalues Visualisation. 141 141 142 144 144 147 147 113 116 117 119 120 123
xii Contents 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. 148 Exercises and Problems. Midterm Exam, November 2015 . 10.1.1 Solutions: Midterm Exam, November 2015. Exam, January 2016. 10.2.1 Solutions: Exam, January 2016. Midterm Exam, November 2016. 10.3.1 Solutions: Midterm Exam, November 2016. Exam, January 2017. 10.4.1 Solutions: Exam, January 2017. Midterm Exam, November 2017. 10.5.1 Solutions: Midterm Exam, November 2017. Exam, January 2018. 10.6.1 Solutions: Exam, January 2018. Midterm Exam, October 2018. 10.7.1 Solutions: Midterm Exam October
2018. 155 155 157 159 162 164 166 168 172 174 177 179 182 184 187 10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 149 150 154 A The Lagrangian. 191 A.l Main Results. 191 A. 1.1 Solution of the Markowitz Problem. 193 В Parametrisations. 195 B. 1 Confidence Domain for an Estimator of M. 195 B.2 Confidence Domain for an Observation R¡ . 196 Bibliography. 199 Index. 203 |
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| author | Brugière, Pierre |
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| author_role | aut |
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| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519 |
| dewey-search | 519 |
| dewey-sort | 3519 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| index_date | 2024-07-03T15:03:02Z |
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| isbn | 9783030377397 |
| issn | 2192-4333 |
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| record_format | marc |
| series2 | Springer texts in business and economics |
| spelling | Brugière, Pierre Verfasser (DE-588)120929947X aut Quantitative portfolio management with applications in Python Pierre Brugière Cham Springer [2020] xii, 205 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Springer texts in business and economics 2192-4333 Quantitative Finance Statistics for Business, Management, Economics, Finance, Insurance Computer Applications Economics, Mathematical Statistics Application software Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Kapitalanlage (DE-588)4073213-7 gnd rswk-swf Portfoliomanagement (DE-588)4115601-8 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 Online-Ausgabe 978-3-030-37740-3 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=032232417&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brugière, Pierre Quantitative portfolio management with applications in Python Quantitative Finance Statistics for Business, Management, Economics, Finance, Insurance Computer Applications Economics, Mathematical Statistics Application software Python Programmiersprache (DE-588)4434275-5 gnd Finanzmathematik (DE-588)4017195-4 gnd Kapitalanlage (DE-588)4073213-7 gnd Portfoliomanagement (DE-588)4115601-8 gnd Quantitative Methode (DE-588)4232139-6 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4017195-4 (DE-588)4073213-7 (DE-588)4115601-8 (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 Pierre Brugière |
| title_fullStr | Quantitative portfolio management with applications in Python Pierre Brugière |
| title_full_unstemmed | Quantitative portfolio management with applications in Python Pierre Brugière |
| title_short | Quantitative portfolio management |
| title_sort | quantitative portfolio management with applications in python |
| title_sub | with applications in Python |
| topic | Quantitative Finance Statistics for Business, Management, Economics, Finance, Insurance Computer Applications Economics, Mathematical Statistics Application software Python Programmiersprache (DE-588)4434275-5 gnd Finanzmathematik (DE-588)4017195-4 gnd Kapitalanlage (DE-588)4073213-7 gnd Portfoliomanagement (DE-588)4115601-8 gnd Quantitative Methode (DE-588)4232139-6 gnd |
| topic_facet | Quantitative Finance Statistics for Business, Management, Economics, Finance, Insurance Computer Applications Economics, Mathematical Statistics Application software Python Programmiersprache Finanzmathematik Kapitalanlage Portfoliomanagement Quantitative Methode |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032232417&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT brugierepierre quantitativeportfoliomanagementwithapplicationsinpython |