Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19
Davide Bazzana (Fondazione Eni Enrico Mattei); Michele Colturato (University of Pavia); Roberto Savona (University of Brescia)
G11, G12, G14, C63
Agent-Based Model, Representativeness, Unprecedented Events
We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioral heterogeneous agents’ model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event.
Suggested citation: D. Bazzana, M. Colturato, R. Savona, ‘Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19’, Nota di Lavoro 26.2021, Milano, Italy: Fondazione Eni Enrico Mattei