Forecasting stock indices with the COVID-19 infection rate as an exogenous variable

Abstract

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. In this study, the effect of covariates has been analyzed for three stock indices: S & P 500, Morgan Stanley Capital International (MSCI) world stock index, and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Results show that parametric approaches can be good forecasting models for the S & P 500 index and the VIX index. On the other hand, a random walk model can be adopted to forecast the MSCI index. Moreover, among the three random walk forecasting methods for the MSCI index, the naïve method provides the best forecasting model.