Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective

Abstract : 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.
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Chapitre d'ouvrage
Multi-factor models and signal processing techniques: application to quantitative finance, pp.59-116, 2013, 〈10.1002/9781118577387.ch3〉
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https://hal.archives-ouvertes.fr/hal-01632883
Contributeur : Christine Okret-Manville <>
Soumis le : vendredi 10 novembre 2017 - 16:51:28
Dernière modification le : samedi 11 novembre 2017 - 01:14:46

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Serge Darolles, Patrick Duvaut, Emmanuelle Jay. Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective. Multi-factor models and signal processing techniques: application to quantitative finance, pp.59-116, 2013, 〈10.1002/9781118577387.ch3〉. 〈hal-01632883〉

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