學術產出-Theses
Article View/Open
Publication Export
-
題名 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作者 穆罕默德
Mahdi, Muhammad Bin Muhammad貢獻者 蔡政憲
Tsai, Cheng-Hsien
穆罕默德
Muhammad Bin Muhammad Mahdi關鍵詞 EGARCH
OPR
波動性
EGARCH
OPR
Volatility日期 2023 上傳時間 6-Jul-2023 16:34:39 (UTC+8) 摘要 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. 參考文獻 Achala, L., K., J. G., K., P. R., Bishal, G. (2015). Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models. Agricultural Economics Research Review. 28(1), 73-82. Arnold, T. W. (2010). Uninformative Parameters and Model Selection Using Akaike’s Information Criterion. Journal of Wildlife Management. 74(6), 1175-1178. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 31, 307-327. Chang, T. Y., Hartzmark, S. M., Solomon, D. H., Soltes, E. F. (2016). Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns. The Review of Financial Studies. 30, 281–323. Chen, J., Hong, H. (2002). Discussion of “Momentum and Autocorrelation in Stock Returns”. The Review of Financial Studies. 15, 565–574. Chou, R. Y. (1988). Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH. Journal of Applied Econometrics. 3(4), 279-294. Dash, R., Dash, P. K., Bisoi, R.T. (2015). A Differential Harmony Search Based Hybrid Interval Type2 Fuzzy EGARCH Model for Stock Market Volatility Prediction. International Journal of Approximate Reasoning. 59, 81-104. Dehay, D., Leskow, J. (1995). Testing stationarity for stock market data. Economics Letters. 50, 205-212. Domain D. L., Louton, D. A. (1997). A threshold autoregressive analysis of stock returns and real economic activity. International Review of Economics and Finance. 6,167-179. Endri, E., Abidin, Z., Simanjuntak, T. P., Nurhayati, I. (2020). Indonesian Stock Market Volatility: GARCH Model. Montenegrin Journal of Economics. 16(2), 7-17. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, Journal of The Econometric Society. 50, 987-1007. Fama E. F. (1965). Random Walks in Stock Market Prices. Taylor & Francis, Ltd. 21(5), 55-59. Faugère, Christophe and Shawky, Hany A. (2005). Volatility and Institutional Investor Holdings in a Declining Market: A Study of NASDAQ During the Year 2000. Available at SSRN: https://ssrn.com/abstract=480982 Gultekin, M. N., Gultekin, N. B. (1983). Stock Market Seasonality International Evidence. Journal of Financial Economics. 12, 469-481. Hinich, M. J., Patterson, D. M. (1985). Evidence of Nonlinearity in Daily Stock Returns. Journal of Business & Economic Statistics. 3, 69-77. Hoque, H. A. A. B., Kim, J. H., Pyun, C. S. (2016). A comparison of variance ratio tests of random walk: A case of Asian emerging stock markets. International Review of Economics and Finance. 16, 488-502. Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics. 12, 13-32. Lewellen, J. (2002). Momentum and Autocorrelation in Stock Returns. The Review of Financial Studies. 15, 533–564. Lim, C. M., Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance. 5, 478 – 487. Lim, K. P., Luo, W., Kim, J. H. (2011). Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests. Applied Economics. 45, 953-962. Lukacs, E. (1942). A Characterization of the Normal Distribution. The Annals of Mathematical Statistics. 13, 91-93 Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, Journal of The Econometric Society. 59, 347-370. Pagan, A. R., Schwert, G. W. (1990). Testing for Covariance Stationarity in Stock Market Data. Economics Letters. 33, 165-170. Rozeff, M. S., Kinney, W. R. (1976). Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics. 3, 379-402. Sariannidis, N., Giannarakis, G., Litinas, N., Konteos, G. (2010). GARCH Examination of Macroeconomic Effects on U.S. Stock Market: A Distinction Between the Total Market Index and the Sustainability Index. European Research Studies. 13(1). Schwert, G. W. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance. 44(5), 1115-1153. St. Pierre, E. F. (1998). Estimating EGARCH-M models: Science or art? The Quarterly Review of Economics and Finance. 38(2), 167-180. Wu, Z., Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Royal Society. 460(2046). Yong, J. N. C., Ziaei, S. M., & R Szulczyk, K. (2021). The Impact of Covid-19 Pandemic on Stock Market Return Volatility: Evidence from Malaysia and Singapore. Asian Economic and Financial Review, 11(3), 191–204. 描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
110933058資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110933058 資料類型 thesis dc.contributor.advisor 蔡政憲 zh_TW dc.contributor.advisor Tsai, Cheng-Hsien en_US dc.contributor.author (Authors) 穆罕默德 zh_TW dc.contributor.author (Authors) Muhammad Bin Muhammad Mahdi en_US dc.creator (作者) 穆罕默德 zh_TW dc.creator (作者) Mahdi, Muhammad Bin Muhammad en_US dc.date (日期) 2023 en_US dc.date.accessioned 6-Jul-2023 16:34:39 (UTC+8) - dc.date.available 6-Jul-2023 16:34:39 (UTC+8) - dc.date.issued (上傳時間) 6-Jul-2023 16:34:39 (UTC+8) - dc.identifier (Other Identifiers) G0110933058 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145807 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營管理英語碩士學位學程(IMBA) zh_TW dc.description (描述) 110933058 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 1. Introduction of the Malaysia Stock Market 1 2. The Overview of the OPR and Volatility 2 3. Research Purpose 6 4. Time Series Concepts 7 4.1 White Noise 7 4.2 Random Walk 8 4.3 Stationarity 10 4.4 Seasonality 12 4.5 Autocorrelation 14 4.6 Autoregressive (AR) 16 4.7 ARCH 19 5. Literature Review 21 5.1 GARCH 22 5.2 EGARCH 23 6. Data and Methodology 25 7. Empirical Results 26 7.1 Descriptive Statistics 26 7.2 Model Results 30 7.3 Prediction Analysis 33 8. Conclusions 36 9. References 37 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110933058 en_US dc.subject (關鍵詞) EGARCH zh_TW dc.subject (關鍵詞) OPR zh_TW dc.subject (關鍵詞) 波動性 zh_TW dc.subject (關鍵詞) EGARCH en_US dc.subject (關鍵詞) OPR en_US dc.subject (關鍵詞) Volatility en_US dc.title (題名) 馬來西亞股市回報率波動模式的建模：GARCH 與 EGARCH zh_TW dc.title (題名) Modelling of Volatility Patterns on Malaysia Stock Market Returns: GARCH vs EGARCH en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Achala, L., K., J. G., K., P. R., Bishal, G. (2015). Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models. Agricultural Economics Research Review. 28(1), 73-82. Arnold, T. W. (2010). Uninformative Parameters and Model Selection Using Akaike’s Information Criterion. Journal of Wildlife Management. 74(6), 1175-1178. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 31, 307-327. Chang, T. Y., Hartzmark, S. M., Solomon, D. H., Soltes, E. F. (2016). Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns. The Review of Financial Studies. 30, 281–323. Chen, J., Hong, H. (2002). Discussion of “Momentum and Autocorrelation in Stock Returns”. The Review of Financial Studies. 15, 565–574. Chou, R. Y. (1988). Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH. Journal of Applied Econometrics. 3(4), 279-294. Dash, R., Dash, P. K., Bisoi, R.T. (2015). A Differential Harmony Search Based Hybrid Interval Type2 Fuzzy EGARCH Model for Stock Market Volatility Prediction. International Journal of Approximate Reasoning. 59, 81-104. Dehay, D., Leskow, J. (1995). Testing stationarity for stock market data. Economics Letters. 50, 205-212. Domain D. L., Louton, D. A. (1997). A threshold autoregressive analysis of stock returns and real economic activity. International Review of Economics and Finance. 6,167-179. Endri, E., Abidin, Z., Simanjuntak, T. P., Nurhayati, I. (2020). Indonesian Stock Market Volatility: GARCH Model. Montenegrin Journal of Economics. 16(2), 7-17. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, Journal of The Econometric Society. 50, 987-1007. Fama E. F. (1965). Random Walks in Stock Market Prices. Taylor & Francis, Ltd. 21(5), 55-59. Faugère, Christophe and Shawky, Hany A. (2005). Volatility and Institutional Investor Holdings in a Declining Market: A Study of NASDAQ During the Year 2000. Available at SSRN: https://ssrn.com/abstract=480982 Gultekin, M. N., Gultekin, N. B. (1983). Stock Market Seasonality International Evidence. Journal of Financial Economics. 12, 469-481. Hinich, M. J., Patterson, D. M. (1985). Evidence of Nonlinearity in Daily Stock Returns. Journal of Business & Economic Statistics. 3, 69-77. Hoque, H. A. A. B., Kim, J. H., Pyun, C. S. (2016). A comparison of variance ratio tests of random walk: A case of Asian emerging stock markets. International Review of Economics and Finance. 16, 488-502. Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics. 12, 13-32. Lewellen, J. (2002). Momentum and Autocorrelation in Stock Returns. The Review of Financial Studies. 15, 533–564. Lim, C. M., Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance. 5, 478 – 487. Lim, K. P., Luo, W., Kim, J. H. (2011). Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests. Applied Economics. 45, 953-962. Lukacs, E. (1942). A Characterization of the Normal Distribution. The Annals of Mathematical Statistics. 13, 91-93 Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, Journal of The Econometric Society. 59, 347-370. Pagan, A. R., Schwert, G. W. (1990). Testing for Covariance Stationarity in Stock Market Data. Economics Letters. 33, 165-170. Rozeff, M. S., Kinney, W. R. (1976). Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics. 3, 379-402. Sariannidis, N., Giannarakis, G., Litinas, N., Konteos, G. (2010). GARCH Examination of Macroeconomic Effects on U.S. Stock Market: A Distinction Between the Total Market Index and the Sustainability Index. European Research Studies. 13(1). Schwert, G. W. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance. 44(5), 1115-1153. St. Pierre, E. F. (1998). Estimating EGARCH-M models: Science or art? The Quarterly Review of Economics and Finance. 38(2), 167-180. Wu, Z., Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Royal Society. 460(2046). Yong, J. N. C., Ziaei, S. M., & R Szulczyk, K. (2021). The Impact of Covid-19 Pandemic on Stock Market Return Volatility: Evidence from Malaysia and Singapore. Asian Economic and Financial Review, 11(3), 191–204. zh_TW
