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

Andrea Renzetti

Papers from arXiv.org

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.

Date: 2023-06, Revised 2023-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fdg and nep-rmg
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