Analysis of Saudi stock market volatility by using ARMA and GARCH Models during COVID-19 pandemic
| dc.contributor.advisor | Dr Tim Pryce-Brown | |
| dc.contributor.author | ABDULKARIM SALEM MSAED ALHEJAILI | |
| dc.date | 2020 | |
| dc.date.accessioned | 2022-05-29T10:18:29Z | |
| dc.date.available | 2022-05-29T10:18:29Z | |
| dc.degree.department | Accounting and Finance | |
| dc.degree.grantor | Swansea University - School of Management | |
| dc.description.abstract | Currently, global markets are in a state of instability and turmoil because of the impact of the COVID-19 pandemic, and it has pushed governments all over the world to take emergency actions to protect their people and economy. This study aims to fill the gaps in the analysis of the performance and model the return volatility of TASI in the Saudi market during the COVID-19 pandemic. The study employs the linked time series models identified through a literature review. It uses different autoregressive models such as ARMA and GARCH family models for forecasting. The analysis includes data on daily prices of TASI for the period between January 1, 2015, and June 30, 2020. The study used the information criteria of the SBIC to assess the best models describing the volatility of returns during the selected period while accurately forecasting the true volatility of autoregressive models based on RMSE and MAE equations. The results show that TASI index does not get affected much by an increase in the number of COVID-19 cases in Saudi Arabia because of the economic support provided by the government. Moreover, the ARMA (1,1) model is the best in terms of describing the return volatility. However, when RMSE and MAE were used. It was found that TGARH (1,1) and GARCH (2,2) models are the most appropriate models for the forecast of volatility. | |
| dc.identifier.uri | https://drepo.sdl.edu.sa/handle/20.500.14154/44882 | |
| dc.language.iso | en | |
| dc.title | Analysis of Saudi stock market volatility by using ARMA and GARCH Models during COVID-19 pandemic | |
| sdl.thesis.level | Master | |
| sdl.thesis.source | SACM - United Kingdom |