Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective
Darolles, Serge; Duvaut, Patrick; Jay, Emmanuelle (2013), Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective, in Darolles, Serge; Duvaut, Patrick; Jay, Emmanuelle, Multi-factor models and signal processing techniques: application to quantitative finance, ISTE ; J. Wiley : London ; Hoboken, NJ, p. 59-116. 10.1002/9781118577387.ch3
Type
Chapitre d'ouvrageDate
2013Book title
Multi-factor models and signal processing techniques: application to quantitative financeBook author
Darolles, Serge; Duvaut, Patrick; Jay, EmmanuellePublisher
ISTE ; J. Wiley
Published in
London ; Hoboken, NJ
ISBN
978-1-84821-419-4
Number of pages
184Pages
59-116
Publication identifier
Metadata
Show full item recordAbstract (EN)
This chapter introduces, illustrates and derives both least squares estimation (LSE) and Kalman filter (KF) estimation of the alpha and betas of a return, for a given number of factors that have already been selected. It formalizes the “per return factor model” and the concept of recursive estimate of the alpha and betas. The chapter explains the setup, objective, criterion, interpretation, and derivations of LSE. The setup, main properties, objective, interpretation, practice, and geometrical derivation of KF are also discussed. The chapter also explains the working of LSE and KF. Numerous simulation results are displayed and commented throughout the chapter to illustrate the behaviors, performance and limitations of LSE and KF.Subjects / Keywords
geometrical interpretation; Kalman filtering (KF); least squares estimation (LSE); per return factor modelRelated items
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