Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks

Massimo Guidolin, Davide La Cara and Massimiliano Marcellino

No 21169, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy

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.

Keywords: Conditionally heteroskedastic models; Covid-19; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 E47 G01 (search for similar items in EconPapers)
Pages: 25
Date: 2021
New Economics Papers: this item is included in nep-ets, nep-for, nep-his, nep-mac and nep-rmg
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Citations: View citations in EconPapers (3)

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