Research projects

Project CEMAPRE internal

TitleStock Market Forecasting Accuracy of Asymmetric GARCH Models during the COVID-19 Pandemic
ParticipantsJorge Caiado (Principal Investigator), Francisco Lúcio
SummaryWe 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.