Learning about unprecedented events: Agent-based modelling and the stock market impact of COVID-19

Davide Bazzana, Michele Colturato and Roberto Savona

Finance Research Letters, 2023, vol. 56, issue C

Abstract: We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioural 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.

Keywords: Agent-based model; Representativeness; Unprecedented events; Asset pricing model; Heterogeneous expectations (search for similar items in EconPapers)
JEL-codes: C63 G11 G12 G14 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612323004579
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:56:y:2023:i:c:s1544612323004579

DOI: 10.1016/j.frl.2023.104085

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().