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ORIGINAL RESEARCH article

Front. Public Health
Sec. Health Economics
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1476196

Comparative Analysis of Volatility Forecasting for Healthcare Stock Indices Amid Public Health Crises: A Study Based on the Bayes-CNN Model

Provisionally accepted
Yanguo Li Yanguo Li Ruitao Gu Ruitao Gu *Dezhi Zhao Dezhi Zhao
  • School of Economics, Yunnan University of Finance And Economics, Kunming, Yunnan Province, China

The final, formatted version of the article will be published soon.

    In recent years, public health events have significantly impacted various aspects of human production and daily life, particularly in the domains of disease transmission and economic stability. While many scholars have primarily focused on the influence of public health events from the perspective of disease prevention and control, research examining their economic implications, especially regarding public health indices in the securities market, remains relatively scarce. Such studies are crucial for ensuring public health safety and stability. This paper employs the Bayesian Convolutional Neural Network (Bayes-CNN) model to predict financial market volatility influenced by public health events and conducts a comparative analysis. To validate the feasibility of this method, the model is used to analyze the impact of the COVID-19 pandemic on the CSI (China Securities Index) Medical Service Index.The results indicate significant differences in the volatility of the China CSI Medical Service Index before and after the outbreak, particularly during the pandemic period. This study also enhances the validity and reliability of its conclusions by incorporating European data and employing the GARCH model. Relevant institutions and individual investors should adopt different regulatory and investment strategies based on the specifics of various public health events to prevent the outbreak of systemic financial risks that could affect social stability. This paper offers a new perspective and methodology for predicting financial market volatility under the influence of public health events, providing valuable insights for investors and decision-makers to better understand and respond to the potential impacts of such events on financial markets.This study aims to investigate the impact of public health events on the volatility of the CSI Medical Service Index and to conduct a comparative analysis of volatility predictions across three periods: before the pandemic, during the pandemic, and after the pandemic. The research seeks to reveal the specific effects of the pandemic on the volatility of healthcare stock indices. This study holds

    Keywords: Public health crises, Healthcare Stock Indices, Volatility forecasting, Bayes-CNN Model, comparative analysis

    Received: 05 Aug 2024; Accepted: 21 Oct 2024.

    Copyright: © 2024 Li, Gu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ruitao Gu, School of Economics, Yunnan University of Finance And Economics, Kunming, Yunnan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.