Bayesian Estimation of Macro-Finance DSGE Models with Stochastic Volatility

64 Pages Posted: 23 Oct 2019

See all articles by David Rapach

David Rapach

Saint Louis University; Washington University in St. Louis

Fei Tan

Saint Louis University

Date Written: October 9, 2019

Abstract

Researchers are increasingly turning to dynamic stochastic general equilibrium (DSGE) models to analyze the structural foundations of risk premia. However, existing DSGE studies of risk premia rarely incorporate stochastic volatility, despite its popularity in empirical asset pricing and growing importance in empirical macroeconomics. We extend the existing literature by developing a Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating risk premia in DSGE models with stochastic volatility. We first propose a Bayesian procedure for estimating a stochastic volatility process in levels and then integrate the procedure into a larger MCMC algorithm that incorporates an affine model solution based on log-normality. The larger MCMC algorithm makes likelihood-based estimation of risk premia in DSGE models with stochastic volatility computationally feasible and efficient. We use the algorithm to estimate the US equity risk premium in a DSGE model with recursive preferences that includes time-preference, technology, investment, and volatility shocks. Time-preference and technology shocks are primarily responsible for the sizable equity risk premium in the estimated DSGE model. The estimated historical stochastic volatility and equity risk premium series display pronounced countercyclical fluctuations.

Keywords: Stochastic volatility, Gibbs sampler, Tailored proposal density, Affine solution, Equity risk premium, Risk-free rate, Structural shocks, Business cycle

JEL Classification: C11, C58, E32, E44, G12

Suggested Citation

Rapach, David and Tan, Fei, Bayesian Estimation of Macro-Finance DSGE Models with Stochastic Volatility (October 9, 2019). Available at SSRN: https://ssrn.com/abstract=3469356 or http://dx.doi.org/10.2139/ssrn.3469356

David Rapach (Contact Author)

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Fei Tan

Saint Louis University ( email )

3674 Lindell Boulevard
Saint Louis, MO 63108
United States

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