The aim of this thesis is to analyze the impact of the COVID-19 pandemic on stock markets, with a specific focus on the Ghanaian Stock Exchange. Using the ARIMA and EGARCH models, this study explores the influence of COVID-19 on stock market volatility, based on daily data from the Ghana Stock Exchange Composite Index from October 2018 to August 2020.
Table of Contents
1. Introduction
2. Literature Review
3. Methodology
3.1 Specification of the ARIMA Model
3.2 Exponential GARCH Model
3.3 Diagnostics test for the EGARCH
4. Results and Discussions
4.1 Estimates of ARIMA Model
5. Conclusion
Research Objectives and Themes
The primary objective of this study is to forecast the volatility of the Ghana Stock Exchange Composite Index and examine the potential asymmetric effects caused by the COVID-19 pandemic during the period from October 2018 to August 2020, providing a basis for informed investor decision-making.
- Analysis of the impact of the COVID-19 pandemic on global and local stock markets.
- Application of the ARIMA model for time-series forecasting of stock indices.
- Utilization of the EGARCH approach to evaluate volatility and asymmetric shock effects.
- Assessment of stock market stability and risk in the context of global economic crises.
- Evaluation of policy responses and their effectiveness in mitigating financial market shocks.
Excerpt from the Book
Methodology
The study employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the Ghana Stock exchange composite index after a period of the 8-months of shock of COVID-19 to Ghana and the world at large.
The ARIMA model is also sometimes called the Box Jenkins (2019) methodology. The model uses information derived from its past behaviors to forecast its trend. It is a univariate model and where the variable itself is regressed on its pass value. It uses the philosophy of “let variable speak for itself”. There are two underlying assumptions of ARIMA modeling. The first assumption is a concern with the stationarity of the time series in question. The series must exhibit mean reversions, has infinite, and time-invariant variance and must also have a theoretical correlogram that diminishes as the lag length increases. The second is the invertibility assumption which requires that the series should be able to be represented by a finite order MA or convergent autoregressive process. It is also the ACF and PACF for identification. It also implicitly assumes that the series can be approximated by the AR model.
Summary of Chapters
Introduction: This chapter contextualizes the global economic crises triggered by COVID-19 and outlines the study's focus on the Ghana Stock Exchange.
Literature Review: This section provides a comprehensive overview of global pandemic-related policies and existing empirical studies on stock market volatility.
Methodology: This chapter details the technical framework, including the mathematical specifications of the ARIMA and EGARCH models used for data analysis.
Results and Discussions: This chapter presents the empirical findings, including unit root tests, ARIMA model estimations, and the results of the EGARCH diagnostics.
Conclusion: This chapter synthesizes the findings, noting that while COVID-19 impacted the index, volatility trends suggest cautious optimism for future informed decision-making.
Keywords
COVID-19, Stock Exchange, ARIMA, GARCH, EGARCH, Volatility, Forecasting, Ghana Stock Exchange Composite Index, Time Series, Market Risk, Asymmetric Effect, Economic Crisis, Financial Modeling, Stock Indices, Pandemic Impact.
Frequently Asked Questions
What is the core focus of this research paper?
The paper examines the impact of the COVID-19 pandemic on the stock market volatility of the Ghana Stock Exchange and provides a forecast for its future trend.
What are the primary thematic areas covered?
The study covers global economic impacts, stock market behavior under crisis conditions, statistical modeling of time series data, and the evaluation of investor risk.
What is the main objective or research question?
The research aims to determine the extent of volatility and asymmetric effects on the Ghana Stock Exchange due to COVID-19 and to provide a forecast that aids investors in their decision-making processes.
Which scientific methods are employed?
The authors use the Autoregressive Integrated Moving Average (ARIMA) model for forecasting and the Exponential GARCH (EGARCH) model to analyze volatility and asymmetric effects.
What topics are discussed in the main body?
The main body discusses the stationarity of the index, the specification and estimation of the models, diagnostic testing, and the interpretation of statistical results regarding stock market performance.
Which keywords characterize this work?
Key terms include COVID-19, Stock Exchange, ARIMA, GARCH, volatility, market risk, and forecasting.
How does the ARIMA model assist in this study?
The ARIMA model allows the researchers to forecast future closing prices of the Ghana Stock Exchange Composite Index based on historical data patterns.
What did the EGARCH analysis reveal about market volatility?
The EGARCH analysis showed that while there was market turbulence, the bad news related to COVID-19 did not have a significant impact on the specific volatility of the Ghana Stock Exchange Composite Index.
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- Anthony Abaidoo (Autor:in), Aminu Osman (Autor:in), Justina Antwi-Konadue (Autor:in), 2022, COVID-19's Impact on Ghana's Stock Market. Analysis and Predictions with ARIMA and EGARCH Models, München, GRIN Verlag, https://www.grin.com/document/1370264
