Filtering Methods for the Estimation of the Long-Run Risk Asset Pricing Model

49 Pages Posted: 10 Nov 2016

See all articles by Eva-Maria Küchlin

Eva-Maria Küchlin

Eberhard Karls Universitaet Tuebingen

Date Written: November 10, 2016

Abstract

Previous attempts to estimate the long-run risk (LRR) model revealed serious methodological issues and low estimation precision of the existing econometric approaches. However, this study shows that despite the presence of latent variables asymptotically efficient maximum likelihood (ML) estimation is possible through application of filtering methods. A three-step estimation strategy is suggested that involves ML estimation relying on the Kalman filter and a particle filter, which allows to identify all LRR model parameters. A Monte Carlo study assesses the estimation precision for different sample sizes, an empirical application presents estimation results obtained from U.S. data.

Keywords: asset pricing, long-run risk, state-space models, maximum likelihood estimation, Kalman filter, particle filter

JEL Classification: C58, G10, G12

Suggested Citation

Küchlin, Eva-Maria, Filtering Methods for the Estimation of the Long-Run Risk Asset Pricing Model (November 10, 2016). Available at SSRN: https://ssrn.com/abstract=2867519 or http://dx.doi.org/10.2139/ssrn.2867519

Eva-Maria Küchlin (Contact Author)

Eberhard Karls Universitaet Tuebingen ( email )

Mohlstrasse 36
Tuebingen, 72074
Germany

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