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Multi-factor Models and Signal Processing Techniques: Application to Quantitative Finance [Hardback]

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  • Format: Hardback, 186 pages, height x width x depth: 241x163x22 mm, weight: 490 g
  • Pub. Date: 26-Jul-2013
  • Publisher: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848214197
  • ISBN-13: 9781848214194
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  • Price: 190,38 €
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Multi-factor Models and Signal Processing Techniques: Application to Quantitative FinanceLarger Image
  • Format: Hardback, 186 pages, height x width x depth: 241x163x22 mm, weight: 490 g
  • Pub. Date: 26-Jul-2013
  • Publisher: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848214197
  • ISBN-13: 9781848214194
Other books in subject:
Permanent link: https://www.kriso.ee/db/9781848214194.html
Keywords:

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 interesting alternative to the selection of factors (both fundamentals and statistical factors) and can provide more efficient estimation procedures, based on lq regularized Kalman filtering for instance.
With numerous illustrative examples from stock markets, this book meets the needs of both finance practitioners and graduate students in science, econometrics and finance.

Contents

Foreword, Rama Cont.
1. Factor Models and General Definition.
2. Factor Selection.
3. Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective.
4. A Regularized Kalman Filter (rgKF) for Spiky Data.
Appendix: Some Probability Densities.

About the Authors

Serge Darolles is Professor of Finance at Paris-Dauphine University, Vice-President of QuantValley, co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals.
Patrick Duvaut is currently the Research Director of Telecom ParisTech, France. He is co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His fields of expertise encompass statistical signal processing, digital communications, embedded systems and QUANT finance.
Emmanuelle Jay is co-founder and President of QAMLab SAS. She has worked at Aequam Capital as co-head of R&D since April 2011 and is member of the Quantitative Management Initiative (QMI) scientific committee. Her research interests include SP for finance, quantitative and statistical finance, and hedge fund analysis.

Foreword xi
Rama Cont
Introduction xv
Notations and Acronyms xxi
Chapter 1 Factor Models and General Definition 1(22)
1.1 Introduction
1(1)
1.2 What are factor models?
2(5)
1.2.1 Notations
2(2)
1.2.2 Factor representation
4(3)
1.3 Why factor models in finance?
7(4)
1.3.1 Style analysis
7(3)
1.3.2 Optimal portfolio allocation
10(1)
1.4 How to build factor models?
11(3)
1.4.1 Factor selection
11(2)
1.4.2 Parameters estimation
13(1)
1.5 Historical perspective
14(4)
1.5.1 CAPM and Sharpe's market model
14(3)
1.5.2 APT for arbitrage pricing theory
17(1)
1.6 Glossary
18(5)
Chapter 2 Factor Selection 23(36)
2.1 Introduction
23(1)
2.2 Qualitative know-how
24(7)
2.2.1 Fama and French model
25(1)
2.2.2 The Chen et al. model
26(1)
2.2.3 The risk-based factor model of Fung and Hsieh
27(4)
2.3 Quantitative methods based on eigenfactors
31(5)
2.3.1 Notation
32(1)
2.3.2 Subspace methods: the Principal Component Analysis
33(3)
2.4 Model order choice
36(2)
2.4.1 Information criteria
36(2)
2.5 Appendix 1: Covariance matrix estimation
38(8)
2.5.1 Sample mean
39(1)
2.5.2 Sample covariance matrix
40(3)
2.5.3 Robust covariance matrix estimation: M-estimators
43(3)
2.6 Appendix 2: Similarity of the eigenfactor selection with the MUSIC algorithm
46(2)
2.7 Appendix 3: Large panel data
48(8)
2.7.1 Large panel data criteria
49(7)
2.8
Chapter 2 highlights
56(3)
Chapter 3 Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective 59(58)
3.1 Introduction
59(1)
3.2 Why LSE and KF in factor modeling?
60(2)
3.2.1 Factor model per return
60(1)
3.2.2 Alpha and beta estimation per return
61(1)
3.3 LSE setup
62(1)
3.3.1 Current observation window and block processing
62(1)
3.3.2 LSE regression
62(1)
3.4 LSE objective and criterion
63(1)
3.5 How LSE is working (for LSE users and programmers)
64(1)
3.6 Interpretation of the LSE solution
65(5)
3.6.1 Bias and variance
65(1)
3.6.2 Geometrical interpretation of LSE
66(4)
3.7 Derivations of LSE solution
70(1)
3.8 Why KF and which setup?
71(3)
3.8.1 LSE method does not provide a recursive estimate
71(1)
3.8.2 The state space model and its recursive component
72(1)
3.8.3 Parsimony and orthogonality assumptions
73(1)
3.9 What are the main properties of the KF model?
74(2)
3.9.1 Self-aggregation feature
74(1)
3.9.2 Markovian property
75(1)
3.9.3 Innovation property
75(1)
3.10 What is the objective of KF?
76(1)
3.11 How does the KF work (for users and programmers)?
77(4)
3.11.1 Algorithm summary
77(3)
3.11.2 Initialization of the KF recursive equations
80(1)
3.12 Interpretation of the KF updates
81(5)
3.12.1 Prediction filtering, equation [ 3.34]
81(1)
3.12.2 Prediction accuracy processing, equation [ 3.35]
82(1)
3.12.3 Correction filtering equations [ 3.36]-[ 3.37]
83(1)
3.12.4 Correction accuracy processing, equation [ 3.38]
84(2)
3.13 Practice
86(18)
3.13.1 Comparison of the estimation methods on synthetic data
86(6)
3.13.2 Market risk hedging given a single-factor model
92(5)
3.13.3 Hedge fund style analysis using a multi-factor model
97(7)
3.14 Geometrical derivation of KF updating equations
104(8)
3.14.1 Geometrical interpretation of MSE criterion and the MMSE solution
104(2)
3.14.2 Derivation of the prediction filtering update
106(1)
3.14.3 Derivation of the prediction accuracy update
106(1)
3.14.4 Derivation of the correction filtering update
107(4)
3.14.5 Derivation of the correction accuracy update
111(1)
3.15 Highlights
112(4)
3.16 Appendix: Matrix inversion lemma
116(1)
Chapter 4 A Regularized Kalman Filter (rgKF) for Spiky Data 117(16)
4.1 Introduction
117(2)
4.2 Preamble: statistical evidence on the KF recursive equations
119(1)
4.3 Robust KF
119(2)
4.3.1 RKF description
119(2)
4.4 rgKF: the rgKF(NG,lq)
121(7)
4.4.1 rgKF description
121(4)
4.4.2 rgKF performance
125(3)
4.5 Application to detect irregularities in hedge fund returns
128(2)
4.6 Conclusion
130(1)
4.7
Chapter highlights
130(3)
Appendix: Some Probability Densities 133(8)
Conclusion 141(2)
Bibliography 143(10)
Index 153
Serge Darolles is Professor of Finance at Paris-Dauphine University, Vice-President of QuantValley, co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals.

Patrick Duvaut is currently the Research Director of Telecom ParisTech, France. He is co-founder of QAMLab SAS, and a member of the Quantitative Management Initiative (QMI) scientific committee. His fields of expertise encompass statistical signal processing, digital communications, embedded systems and QUANT finance.

Emmanuelle Jay is co-founder and President of QAMLab SAS. She has worked at Aequam Capital as co-head of R&D since April 2011 and is member of the Quantitative Management Initiative (QMI) scientific committee. Her research interests include SP for finance, quantitative and statistical finance, and hedge fund analysis.