Implementing Stochastic Volatility in DSGE Models: A Comment

22 Pages Posted: 21 Oct 2017 Last revised: 1 Jul 2018

Lorenzo Bretscher

London Business School - Department of Finance; affiliation not provided to SSRN

Alex Hsu

Georgia Institute of Technology - Scheller College of Business

Andrea Tamoni

London School of Economics & Political Science (LSE)

Date Written: December 17, 2017

Abstract

We highlight a state variable misspecification with one accepted method to implement stochastic volatility (SV) in DSGE models when transforming the nonlinear state-innovation dynamics to its linear representation. Although the technique is more efficient numerically, we show that it is not exact but only serves as an approximation when the magnitude of SV is small. Not accounting for this approximation error may induce substantial spurious volatility in macroeconomic series, which could lead to incorrect inference about the performance of the model. We also show that, by simply lagging and expanding the state vector, one can obtain the correct state-space specification. Finally, we validate our augmented implementation approach against an established alternative through numerical simulation.

Keywords: Dynamic equilibrium economies, Stochastic volatility, Perturbation, Matlab code

JEL Classification: C63, C68, E37

Suggested Citation

Bretscher, Lorenzo and Hsu, Alex and Tamoni, Andrea, Implementing Stochastic Volatility in DSGE Models: A Comment (December 17, 2017). Georgia Tech Scheller College of Business Research Paper No. 17-40. Available at SSRN: https://ssrn.com/abstract=3056250 or http://dx.doi.org/10.2139/ssrn.3056250

Lorenzo Bretscher

London Business School - Department of Finance ( email )

Sussex Place
Regent's Park
London NW1 4SA
United Kingdom

affiliation not provided to SSRN

Alex Hsu

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States
4043851123 (Phone)

Andrea Tamoni (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom
02079557303 (Phone)

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