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

Author

Listed:
  • Davide Bazzana

    (Fondazione Eni Enrico Mattei)

  • Michele Colturato

    (University of Pavia)

  • Roberto Savona

    (University of Brescia)

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.

Suggested Citation

  • Davide Bazzana & Michele Colturato & Roberto Savona, 2021. "Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19," Working Papers 2021.26, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2021.26
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Agent-Based Model; Representativeness; Unprecedented Events;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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