Multi-factor models and signal processing techniques : application to quantitative finance / Serge Darolles, Patrick Duvaut, Emmanuelle Jay.

Format:

Book

Author/Creator:

Darolles, Serge.

Contributor:

Duvaut, Patrick.

Jay, Emmanuelle.

Series:

ISTE

Digital signal and image processing series

Language:

English

Subjects (All):

Signal processing--Mathematical models.

Signal processing.

Finance--Mathematical models.

Finance.

Physical Description:

1 online resource (188 p.)

Edition:

1st ed.

Place of Publication:

London : ISTE, 2013.

Language Note:

English

Summary:

With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages "embedded" quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented "risk assessment-based" practices.This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an intere

Contents:

Cover; Title Page; Contents; Foreword; Introduction; Notations and Acronyms; Chapter 1. Factor Models andGeneral Definition; 1.1. Introduction; 1.2. What are factor models?; 1.2.1. Notations; 1.2.2. Factor representation; 1.3. Why factor models in finance?; 1.3.1. Style analysis; 1.3.2. Optimal portfolio allocation; 1.4. How to build factor models?; 1.4.1. Factor selection; 1.4.2. Parameters estimation; 1.5. Historical perspective; 1.5.1. CAPM and Sharpe's market model; 1.5.2. APT for arbitrage pricing theory; 1.6. Glossary Volatility; Chapter 2. Factor Selection; 2.1. Introduction

2.2. Qualitative know-how2.2.1. Fama and French model; 2.2.2. The Chen et al. model; 2.2.3. The risk-based factor model of Fung and Hsieh; 2.3. Quantitative methods based on eigenfactors; 2.3.1. Notation; 2.3.2. Subspace methods: the Principal Component Analysis; 2.4. Model order choice; 2.4.1. Information criteria; 2.5. Appendix 1: Covariance matrix estimation; 2.5.1. Sample mean; 2.5.2. Sample covariance matrix; 2.5.3. Robust covariance matrix estimation: M-estimators; 2.6. Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm; 2.7. Appendix 3: Large panel data

2.7.1. Large panel data criteria2.8. Chapter 2 highlights; Chapter 3. Least Squares Estimation(LSE) and Kalman Filtering (KF)for Factor Modeling:A Geometrical Perspective; 3.1. Introduction; 3.2. Why LSE and KF in factor modeling?; 3.2.1. Factor model per return; 3.2.2. Alpha and beta estimation per return; 3.3. LSE setup; 3.3.1. Current observation window and block processing; 3.3.2. LSE regression; 3.4. LSE objective and criterion; 3.5. How LSE is working (for LSE users and programmers); 3.6. Interpretation of the LSE solution; 3.6.1. Bias and variance

3.6.2. Geometrical interpretation of LSE3.7. Derivations of LSE solution; 3.8. Why KF and which setup?; 3.8.1. LSE method does not provide a recursive estimate; 3.8.2. The state space model and its recursive component; 3.8.3. Parsimony and orthogonality assumptions; 3.9. What are the main properties of the KF model?; 3.9.1. Self-aggregation feature; 3.9.2. Markovian property; 3.9.3. Innovation property; 3.10. What is the objective of KF?; 3.11. How does the KF work (for users and programmers)?; 3.11.1. Algorithm summary; 3.11.2. Initialization of the KF recursive equations

3.12. Interpretation of the KF updates3.12.1. Prediction filtering, equation [3.34]; 3.12.2. Prediction accuracy processing, equation [3.35]; 3.12.3. Correction filtering equations [3.36]-[3.37]; 3.12.4. Correction accuracy processing, equation [3.38]; 3.13. Practice; 3.13.1. Comparison of the estimation methods on synthetic data; 3.13.2. Market risk hedging given asingle-factor model; 3.13.3. Hedge fund style analysis using amulti-factor model; 3.14. Geometrical derivation of KF updating equations; 3.14.1. Geometrical interpretation of MSE criterion and the MMSE solution

3.14.2. Derivation of the prediction filtering update

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9781118577493

1118577493

9781118577387

1118577388

9781118577400

111857740X

OCLC:

857365255