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

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.

How do mobility restrictions and social distancing during COVID-19 affect oil price?

This paper provides an analysis of the effect of the COVID-19 outbreak on the crude oil price. Using a newly developed air mobility index and Apple's driving trends index, we assess the effect of human mobility restrictions and social distancing during the COVID-19 pandemic on the crude oil price. We apply a quantile regression model, which evaluates different quantiles of the crude oil price.

Impacts of COVID-19 local spread and Google search trend on the US stock market.

We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. We use both conventional econometric and Machine Learning (ML) models that incorporate the local spread dynamics, COVID-19 cases and death, and Google search activities to assess if incorporating information about local spreads improves the predictive accuracy of models for the US stock market. The results suggest that COVID-19 cases and deaths, its local spread, and Google searches have impacts on abnormal stock prices between January 2020 to May 2020.