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

TL;DR

This paper introduces a Bayesian framework using time-varying skewness stochastic volatility models with Skew-Normal and Skew-t shocks to improve macroeconomic risk forecasting, especially for downside risks to GDP growth.

Contribution

It develops efficient Bayesian estimation methods for models with time-varying skewness and demonstrates their effectiveness in macroeconomic risk prediction and analysis.

Findings

Models outperform semi-parametric approaches like quantile regression.

Time-varying skewness is significant in macroeconomic and financial shocks.

The approach provides a competitive alternative for macroeconomic risk forecasting.

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.