Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo

48 Pages Posted: 6 May 2020 Last revised: 24 Oct 2020

See all articles by Andras Fulop

Andras Fulop

ESSEC Business School

Jeremy Heng

affiliation not provided to SSRN

Junye Li

Fudan University - School of Management

Hening Liu

University of Manchester - Alliance Manchester Business School

Date Written: October 13, 2020

Abstract

We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model.

Keywords: Asset Pricing, Long-Run Risk, Autoregressive Gamma Process, Log-linearization, Projection Methods, Particle Filters, Sequential Monte Carlo Sampler

JEL Classification: C11, C32, C58, E44, G12

Suggested Citation

Fulop, Andras and Heng, Jeremy and Li, Junye and Liu, Hening, Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo (October 13, 2020). Available at SSRN: https://ssrn.com/abstract=3573235 or http://dx.doi.org/10.2139/ssrn.3573235

Andras Fulop

ESSEC Business School ( email )

3 Avenue Bernard Hirsch
CS 50105 CERGY
CERGY, CERGY PONTOISE CEDEX 95021
France

HOME PAGE: http://www.andrasfulop.com

Jeremy Heng

affiliation not provided to SSRN

Junye Li (Contact Author)

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

Hening Liu

University of Manchester - Alliance Manchester Business School ( email )

Booth Street West
Manchester, M15 6PB
United Kingdom

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