Quantitative portfolio management: with applications in Python

by Brugière, Pierre

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Title Statements

Full title :Quantitative portfolio management : with applications in Python / Pierre Brugière.

Summary

This self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the translation of all the mathematical concepts in an easily interpretable way. 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.

Edition Statements

Publication Date:©2020

Publication Statements

Publisher(s):Springer
Place of Publication:Cham
Language:eng
Publication Date:©2020

Notes

7.2.3 Expected Shortfall.

The Physical Object

Description:1 online resource (212 pages).

Edition Identifiers

ISBN:3030377407, 9783030377410, 3030377415, 9783030377427, 3030377423, 9783030377403
DOI:10.1007/978-3-030-37740-3
oclc:on1311345454
LC Classification:HG4529.5
Dewey Decimal:332.6

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