Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19

Davide Bazzana, Michele Colturato and Roberto Savona
Additional contact information
Davide Bazzana: Fondazione Eni Enrico Mattei
Michele Colturato: University of Pavia
Roberto Savona: University of Brescia

No 2021.26, Working Papers from Fondazione Eni Enrico Mattei

Abstract: 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.

Keywords: Agent-Based Model; Representativeness; Unprecedented Events (search for similar items in EconPapers)
JEL-codes: C63 G11 G12 G14 (search for similar items in EconPapers)
Date: 2021-10
New Economics Papers: this item is included in nep-cwa, nep-ene and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://feem-media.s3.eu-central-1.amazonaws.com/w ... oads/NDL2021-026.pdf (application/pdf)

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:fem:femwpa:2021.26

Access Statistics for this paper

More papers in Working Papers from Fondazione Eni Enrico Mattei Contact information at EDIRC.
Bibliographic data for series maintained by Alberto Prina Cerai ( this e-mail address is bad, please contact ).