Modelling and Forecasting Macroeconomic Risk with Time Varying Skewness Stochastic Volatility Models

Author:

Andrea Renzetti

Keyword:

Economics, Econometrics, Econometrics (econ.EM)

journal:

date:

2023-06-14 16:00:00

Abstract

Monitoring downside risk and upside risk to the key macroeconomic indicators is critical for effective policymaking aimed at maintaining economic stability. In this paper I propose a parametric framework for modelling and forecasting macroeconomic risk based on stochastic volatility models with Skew-Normal and Skew-t shocks featuring time varying skewness. Exploiting a mixture stochastic representation of the Skew-Normal and Skew-t random variables, in the paper I develop efficient posterior simulation samplers for Bayesian estimation of both univariate and VAR models of this type. In an application, I use the models to predict downside risk to GDP growth in the US and I show that these models represent a competitive alternative to semi-parametric approaches such as quantile regression. Finally, estimating a medium scale VAR on US data I show that time varying skewness is a relevant feature of macroeconomic and financial shocks.

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