政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152474

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152474

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 114105/145137 (79%)
造访人次 : 52158138 在线人数 : 543

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/152474

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/152474

题名:	真實波動度之預測整合總體經濟資訊：以元大台灣50 ETF為例 Forecasting Realized Volatility by Integrating Macroeconomic Information: A Case Study of the Yuanta Taiwan 50 ETF
作者:	陳棣文 Chen, Ti-Wen
贡献者:	廖四郎 Liao, Szu-Lang 陳棣文 Chen, Ti-Wen
关键词:	GARCH-MIDAS 主成分分析極限梯度提升夏普利值貝葉斯超參數 GARCH-MIDAS Principal Component Analysis Extreme Gradient Boosting Shapley Value Bayesian Optimization for Hyperparameter Tuning
日期:	2024
上传时间:	2024-08-05 12:19:08 (UTC+8)
摘要:	波動度為金融商品於一段時間其價格變化累積的觀察指標，其在財務工程領域中有著無與倫比的學術地位，諸多其衍生相關之學術研究領域，舉凡由資產定價模型回推估算的隱含波動度、由高頻資料價格變動累積的真實波動度…等。本文的研究目標以元大台灣50 ETF之日頻率真實波動度預測作為研究標的，利用Engle et al. (2008) 所提出整合相異頻率資料訊息的GARCH-MIDAS方法，整合由主成分分析 (PCA) 萃取過後之低頻總體經濟因子，並參酌Song et al. (2023) 所使用之流動性相關變數，再搭配極限梯度提升 (XGBOOST) 建構預測模型，以五項殘差相關指標 (MAE、MSE、RMSE、SMAPE、RMSPE) 衡量模型成效，最後以Shapley (1951) 所提出的Shapley value 回推機器學習模型的預測邏輯，增強此模型的解釋性。實證結果顯示，GARCH-MIDAS 方法所取得之短期波動度有著低估且無法有效追蹤短期真實波動度型態的問題，但其於長期型態上有著不俗的追蹤能力，故將其整合每日流動性相關變數，並輔以機器學習與貝葉斯超參數 (Bayesian Optimization for Hyperparameter Tuning) 修正能達到很好的預測與短期型態追蹤效果，並於Shapley value 模型解釋時，GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity - Mixed Data Sampling)之短期因子有著非常重要的變數邊際貢獻。 Volatility is an indicator of the accumulated price changes of a financial product over a period of time, which has an unparalleled academic status in financial engineering. There are numerous related academic research fields derived from it, such as implied volatility inferred from asset pricing models, realized volatility accumulated from high-frequency price changes, and so on. The research objective of this paper is to forecast the daily realized volatility of the Yuanta/P-shares Taiwan Top 50 ETF as the research subject. We employ the GARCH-MIDAS model proposed by Engle et al. (2008) to integrate information from mixed-frequency data, incorporating the low-frequency macroeconomic factors extracted by principal component analysis (PCA) and considering the liquidity-related variables used by Song et al. (2023). We then construct a forecasting model using extreme gradient boosting (XGBoost) and evaluate the model performance using five residual-related metrics (MAE, MSE, RMSE, SMAPE, RMSPE). Finally, we use the Shapley value proposed by Shapley (1951) to explain the prediction logic of the machine learning model, enhancing its interpretability. The empirical results show that the short-term volatility obtained by the GARCH-MIDAS model has problems of underestimation and inability to effectively track short-term realized volatility patterns. However, it has decent tracking ability for long-term patterns. By integrating daily liquidity-related variables and correcting with machine learning and Bayesian optimization for hyperparameter tuning, we can achieve good forecasting and short-term pattern tracking performance. In the Shapley value model explanation, the short-term factor of GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity - Mixed Data Sampling) has a very important variable marginal contribution.
參考文獻:	陳慶賢 (2018)。使用 GARCH-MIDAS 模型對台灣股市波動度進行建模和預測。應用經濟研究, 11(2), 115-130。王昱成, 吳宗杰 (2017)。GARCH-MIDAS 模型在台灣股市波動度建模中的應用：實證研究。風險與金融管理學報, 10(4), 43。 Alaali, F. (2020). Forecasting stock market volatility using GARCH, value-at-risk and expected shortfall models: Evidence from Saudi Arabia. Quarterly Review of Economics and Finance, 78, 27-43. Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: a GARCH‐MIDAS approach. Journal of Forecasting, 32(7), 600-612. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Chen, N. F. (2018). Stock volatility forecasting and model selection. Review of Quantitative Finance and Accounting, 50(1), 185-221. Dong, J. (2022). Modelling and forecasting long-term volatility with Markov-switching asymmetric jump GARCH–MIDAS models. Journal of Forecasting, 41(6), 988-1012. Engle, R. F., Ghysels, E., & Sohn, B. (2008). On the economic sources of stock market volatility. NYU Working Paper No. FIN-08-046. Ghysels, E., Qian, H., & Zhang, X. (2019). Machine learning methods for financial applications. Available at SSRN 3475723. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307-317. Song, Y., Tang, X., Wang, H., & Ma, Z. (2023). Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH-MIDAS and deep learning models. Journal of Forecasting, 42(3), 243-267. Taha, A. A., & Malebary, S. J. (2020). An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access, 8, 79184-79200. Tong, E., Lim, C., Zhu, A., Cheng, D., & Wolters, J. (2020). A hybrid GARCH-MIDAS approach to modeling and forecasting stock market volatility. The Journal of Risk Finance. Zhu, Y., & Shao, X. (2017). Stock volatility forecasting using GARCH-MIDAS-XGBoost model. Finance Research Letters, 22, 38-42. Zhou, X., Pan, W., & Xiao, Y. (2021). Stock market volatility forecasting with XGBoost- Embedded Residual Neural Networks. Expert Systems with Applications, 178, 114962.
描述:	碩士國立政治大學金融學系 111352035
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111352035
数据类型:	thesis
显示于类别:	[金融學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
203501.pdf		3024Kb	Adobe PDF	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈