Template-type: ReDIF-Paper 1.0
Author-Name: Massimo Guidolin
Author-Name: Davide La Cara
Author-Name: Massimiliano Marcellino
Title: Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks
Abstract: With reference to S&P 500 daily returns, we report evidence of an in-sample predictive accuracy breakdown for realized variance by GARCH models in correspondence to the March 2020 Covid-19 outbreak. However, a variety of macroeconomic risk, political and social media sentiment uncertainty factors, and crucially a few variables capturing the evolution of the Covid-19 pandemics, successfully predict the direction and size of GARCH forecast errors between November 2019 and June 2020. Predictors related to diagnosed cases, their rate of growth, and the progression of the curve of deceased, infected people in the United States are featured prominently. We test a number of “augmented” GARCH models to include the most precisely estimated exogenous variables and find that they offer precise forecasts in samples that include the Covid-19 outbreak. In genuine out-of-sample tests, augmenting GARCH with Covid-19 related exogenous variables increases the percentage of days in which the direction of change in realized variance is correctly predicted.
Classification-JEL: C32, C53, E47, G01
Keywords: Conditionally heteroskedastic models, Covid-19, volatility forecasting
Length: 25
Number: 21169
Creation-Date: 2021
File-URL: https://repec.unibocconi.it/baffic/baf/papers/cbafwp21169.pdf
File-Format: application/pdf
File-Size: 658
Handle: RePEc:baf:cbafwp:cbafwp21169