Project CEMAPRE internal
Title | Stock Market Forecasting Accuracy of Asymmetric GARCH Models during the COVID-19 Pandemic |
Participants | Jorge Caiado (Principal Investigator), Francisco Lúcio |
Summary | We are going to develop a new clustering approach for comparing financial time series and employ it to study how the COVID-19 pandemic affected the U.S. stock market. Essentially, we will compute the forecast accuracy of asymmetric GARCH models applied to S&P500 industries and use the model forecast errors for different horizons and cut-off points to calculate a distance matrix for the stock indices. Hierarchical and non-hierarchical clustering algorithms will be used to assign the set of industries into clusters. |