GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy

Abstract

COVID-19 pandemic has led to uncertainties in the financial markets around the globe. The pandemic has caused volatilities in the financial market at varying magnitudes, in the emerging versus developed economy. To examine this phenomenon, this study investigates the impact of COVID-19 pandemic on stock market returns and volatility in an emerging economy, i.e., Indonesia, versus developed country, i.e., Hungary, using an event-study approach methodology utilizing GARCH (1,1) model. In this study, the Jakarta Composite Index (JCI) and the b (BUX) data were obtained from Investing and Bloomberg, covering two global events observed within the selected period from 27 September 2006 to 31 August 2021. The data is compared with the stock market volatility data from the global financial crisis in 2007/08. Findings reveal that the recent COVID-19 pandemic had negative stock market returns at a greater magnitude compared to the global financial crisis, in both the emerging and developed economy’s equity market. Stock markets in Indonesia and Hungary have experienced volatility during the crisis. While comparing the result between COVID-19 and the global financial crisis, we found that the volatility on the stock markets is higher in the COVID-19 pandemic than during the global financial crisis. The higher stock market negative returns and volatility during the COVID-19 pandemic triggered the lockdown and limited economic activities, which impacted supply and demand shock. The virus’s propagation and mutation are continually evolving, reminding us that the pandemic is far from over. Developed countries with larger fiscal space seem to find it easier to make responsive policies than countries with a tighter financial budget. Fiscal and monetary policies seem to be a quick solution to stabilize the economy and maintain investor confidence in the Indonesian and Hungarian capital markets. Furthermore, the extension of stock market volatility understanding ensures relevant information for investors, which benefits to mitigate the risk and build sustainable investments of the unprecedented events and enables the promotion of Sustainable Development Goal number 8 (SDG8) to communities, with access to financial products including the stock market, especially during economic and financial uncertainties.

The Egyptian stock market's reaction to the COVID-19 pandemic

“…To model stock market volatility for the entire study period, the GARCH model was applied. GARCH (1,1) was used because it is the most popular and robust model compared with other specifications (Brooks, 2008), it became a pioneer to model the volatility in the financial time series (Setiawan et al, 2021). Estimating the GARCH model requires stationary data; therefore, Phillips-Perron and augmented Dickey-Fuller unit root tests are used to check for stationarity.…”

“…To model stock market volatility for the entire study period, the GARCH model was applied. GARCH (1,1) was used because it is the most popular and robust model compared with other specifications (Brooks, 2008), it became a pioneer to model the volatility in the financial time series (Setiawan et al. , 2021).…”

“…In developed countries, Simsek and Ozkan (2020) concluded that confirmed cases negatively affected stock market return. Setiawan (2021) found that the negative effect of COVID-19 on stock market returns was greater than that of the global financial crisis in both the emerging and developed economies. Surprisingly, some studies concluded that COVID-19 had an insignificant effect on the performance of stock markets in developing countries.…”

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

“…The traditional econometric and statistical methods, including autoregressive integrated moving average (ARIMA) models ( 10 , 11 ), vector error correction model ( 12 ), generalized autoregressive conditional heteroscedasticity model ( 13 ), grey forecast model ( 14 ), exponential smoothing ( 15 ), causal analysis models, such as multiple linear regression ( 16 ), vector autoregression ( 17 ), threshold autoregression ( 18 , 19 ), are widely utilized to predict economic time series. These models have good interpretability and good performance based on linear assumptions.…”

Self-Weighted LSE and Residual-Based QMLE of ARMA-GARCH Models

“…Zhang and Zhang (2020) showed that the GARCHbased option-pricing models are able to price the SPX one-month variance swap rate, that is, the CBOE Volatility Index (VIX) accurately. Setiawan et al (2021) used the GARCH(1, 1) model to analyze stock market turmoil during COVID-19 outbreak in an emerging and developed Economy.…”