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| Title: | 馬來西亞股市回報率波動模式的建模：GARCH 與 EGARCH
Modelling of Volatility Patterns on Malaysia Stock Market Returns: GARCH vs EGARCH |
| Authors: | 穆罕默德
Mahdi, Muhammad Bin Muhammad |
| Contributors: | 蔡政憲
Tsai, Cheng-Hsien
穆罕默德
Muhammad Bin Muhammad Mahdi |
| Keywords: | EGARCH
OPR
波動性
EGARCH
OPR
Volatility |
| Date: | 2023 |
| Issue Date: | 2023-07-06 16:34:39 (UTC+8) |
| Abstract: | Stock price volatility pattern is an imperative aspect of financial analysis, and the use of machine learning models using python has become increasingly important. Two popular models for stock price prediction are the symmetric generalised autoregressive conditional heteroskedasticity (GARCH) and the asymmetric exponential generalised autoregressive conditional heteroskedasticity (EGARCH) models. This study aims to compare the effectiveness of GARCH and EGARCH models in analysing the volatility of the Malaysia stock market.
To conduct this study, I collected five year daily stock price data of FTSE Bursa Malaysia KLCI (FBM KLCI) listed on the Bursa Malaysia stock exchange from January 1, 2018, to December 31, 2022. I used this data to train and test both GARCH and EGARCH models and compared their performance in analysing long-term volatility in the Malaysia stock market. The result shows that EGARCH(1,1) was the best model among the four tested in capturing volatility of the FBM KLCI during the period of frequent OPR hikes. |
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| Description: | 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
110933058 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110933058 |
| Data Type: | thesis |
| Appears in Collections: | [國際經營管理英語碩士學程IMBA] 學位論文
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