Heliyon · 2024 · 10 citations · 42 references
BusinessEconomicsFinanceEconometricsFinancial EconomicsTime Series AnalysisAsset Return ModelingFinancial CrisesFinancial RiskStochastic VolatilityVolatility AnalysisVolatility ModelingFinancial Market VolatilityGeneralized Autoregressive Conditional Heteroskedasticity ModelsMarket Volatility EffectsStock Market ReturnsTime Series Volatility ModelingTime-varying Volatility DynamicsTime-varying Volatility ModelsVolatility
The COVID-19 pandemic has resulted in significant financial losses globally, increasing the volatility of financial assets. Thus, this study models the stock market volatility of developed economies during the COVID-19 pandemic. For this purpose, we used the GJR-GARCH (Albulescu, 2020; Albulescu, 2020) [1,1] econometric model on the daily time series returns data ranging from 01 st -July-2019 to 18th-November-2020. The entire dataset was equally divided into two subsets; before COVID-19, and after the COVID-19. The empirical results of this study showed the presence of volatility clustering, leverage effect, and excess kurtosis indicating leptokurtic phenomena in all stock indices returns. In addition to this, it can be noted that compared to before COVID-19, the stock markets showed negative returns, and increased volatility during the COVID-19. Hence, based on these findings, this study provides significant insights for global stock market investors and economic policymakers regarding financial portfolio construction particularly during crises times.
42
Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root
David A. Dickey, Wayne A. Fuller · Econometrica · 1981 · 14.6K citations
Business, Economics, Econometrics +15
Financial markets under the global pandemic of COVID-19
Dayong Zhang, Min Hu, Qiang Ji · Finance research letters · 2020 · 2.3K citations · Full text