34,30 €*
Versandkostenfrei per Post / DHL
Lieferzeit 2-3 Wochen
All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data.
This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject.
All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data.
This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject.
Includes exercises based on exam questions
Illustrates and expresses the main results in plain language understandable by the pure financier
Details efficient web data extraction techniques
Enables the reader with a good background in general mathematics to implement most of the results in any chosen market
Returns and the Gaussian Hypothesis.- Utility Functions and the Theory of Choice.- The Markowitz Framework.- Markowitz Without a Risk-Free Asset.- Markowitz with a Risk-Free Asset.- Performance and Diversification Indicators.- Risk Measures and Capital Allocation.- Factor Models.- Identification of the Factors.- Exercises and Problems.
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 220 |
| Reihe: | Springer Texts in Business and Economics |
| Inhalt: |
xii
205 S. 1 s/w Illustr. 22 farbige Illustr. 205 p. 23 illus. 22 illus. in color. |
| ISBN-13: | 9783030377397 |
| ISBN-10: | 3030377393 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Brugière, Pierre |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer Texts in Business and Economics |
| Maße: | 241 x 160 x 18 mm |
| Von/Mit: | Pierre Brugière |
| Erscheinungsdatum: | 29.03.2020 |
| Gewicht: | 0,5 kg |
Includes exercises based on exam questions
Illustrates and expresses the main results in plain language understandable by the pure financier
Details efficient web data extraction techniques
Enables the reader with a good background in general mathematics to implement most of the results in any chosen market
Returns and the Gaussian Hypothesis.- Utility Functions and the Theory of Choice.- The Markowitz Framework.- Markowitz Without a Risk-Free Asset.- Markowitz with a Risk-Free Asset.- Performance and Diversification Indicators.- Risk Measures and Capital Allocation.- Factor Models.- Identification of the Factors.- Exercises and Problems.
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 220 |
| Reihe: | Springer Texts in Business and Economics |
| Inhalt: |
xii
205 S. 1 s/w Illustr. 22 farbige Illustr. 205 p. 23 illus. 22 illus. in color. |
| ISBN-13: | 9783030377397 |
| ISBN-10: | 3030377393 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Brugière, Pierre |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer Texts in Business and Economics |
| Maße: | 241 x 160 x 18 mm |
| Von/Mit: | Pierre Brugière |
| Erscheinungsdatum: | 29.03.2020 |
| Gewicht: | 0,5 kg |
Ähnliche Produkte
Lieferzeit 4-7 Werktage
Lieferzeit 1-2 Wochen
Lieferzeit 2-3 Wochen
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Lieferzeit 2-3 Wochen