Contagion effect of financial markets in crisis: An analysis based on the DCC-MGARCH model

Xiuping Ji, Sujuan Wang, Honggen Xiao, Naipeng Bu (Corresponding Author), Xiaonan Lin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC– MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic are higher than those during the 2008 financial crisis owing to the further opening of the Chinese capital market, and financial institutions’ investments in the European market are higher than those in the American markets; (3) a stepwise increase is observed in the dynamic conditional correlation between the returns on the S&P 500 Index and SSEC during and after the onset of a destructive crisis; and (4) a unidirectional contagion effect exists between the Chinese market and US market, and the Hong Kong stock market contributes to the risk spillover. Effective transmission channels of external negative shocks may be investors’ sentiments, financial institutions, and the RMB exchange rate in the stock markets. This study provides useful suggestions to authorities formulating financial regulations and investors diversifying risk investments.

Original languageEnglish
Article number1819
JournalMathematics
Volume10
Issue number11
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • DCC–MGARCH model
  • Granger causality test
  • correlation
  • global turmoil

ASJC Scopus subject areas

  • General Mathematics

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