全球經濟和金融危機激發了對金融市場與總體經濟、經濟政策不確定性之 間關係的研究，較高的波動性會造成廣泛的恐慌並導致市場混亂，從而降低 投資者的信心並導致商業投資和經濟增長下降，因此本文以 Engle (2013)所 提出的 GARCH-MIDAS 模型，驗證經濟政策不確定性指標(EPU)和總體經濟 變量對台灣股市波動的影響。利用 GARCH-MIDAS 模型可以清楚了解到總 體經濟變量和經濟政策不確定性指標分別對長期市場波動和短期市場波動 個別的影響，也不再像被傳統 GARCH 類模型一樣限制在使用相同頻率資料 ，能更加靈活。因此我們在 GARCH-MIDAS 的框架下建立了兩類型的模型， 首先採用單因子模型分別觀察了已實現波動率、總體經濟變量 CPI 和經濟政 策不確定性指標 EPU 增長率對股票市場波動的影響，再利用雙因子模型觀 察了 CPI 和 EPU 之增長率分別跟本身的實現波動率對股票市場波動的影響 。
在實證上，本研究選取與探討變數相關的 204 筆月資料和 4455 筆日資料 作為研究對象，時間從 2003 年 5 月至 2021 年 4 月，透過總體經濟和經濟政 策不確定性總波動和長期波動圖和配適度 BIC 判斷，EPU 成長率的雙因子 模型對股票市場波動性具有更強的解釋力。

The global economic and financial crisis have inspired research into the relationship between financial markets and the macroeconomics, economic policy uncertainty, and the higher volatility create widespread panic and results in disorderly market situations that reduce investor's confidence and that lead to declines in business investments and economic growth. Therefore, this paper uses the GARCH-MIDAS model proposed by Engle (2013) to verify the impact of economic policy uncertainty (EPU) and macroeconomic variables on the volatility of stock market from Taiwan. Using the GARCH-MIDAS model, we can clearly understand the impact of macroeconomic variables and economic policy uncertainty indicators on long-term component of volatility and short-term component of volatility .It is no longer limited to using the same frequency data as the traditional GARCH model, and it can be more flexible. Therefore, we established two types of models under the framework of GARCH-MIDAS. First, we used a single-factor model to observe the impact of realized volatility, macroeconomic variable CPI, and economic policy uncertainty indicator EPU growth rate on stock market volatility. Then use the two-factor model to observe the impact of the growth rate of CPI and EPU and their own realized volatility on stock market volatility.
In empirical, this research selected 204 monthly data and 4455 daily data as explanatory variable, from May 2003 to April 2021. The two-factor model of the EPU growth rate has a stronger explanatory power for the volatility of the stock market by comparing curves of total volatility and its long-term component of the economic policy uncertainty (EPU) and macroeconomic in plot and the values of BIC.

審定書 i
誌謝 ii
摘要 iii
Abstract iv
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究架構 3
第二章 文獻回顧 4
2.1 實證研究文獻回顧 4
2.1.1 總體經濟變量在股票市場報酬和波動中的作用 4
2.1.2 經濟政策不確定性指標與股票市場報酬和波動的關係 5
2.2 計量模型文獻回顧 5
第三章 經濟模型 10
第四章 計量模型 15
4.1 單根檢定 15
4.1.1 DF單根檢定 15
4.1.2 ADF單根檢定 18
4.2 落後數的決定 20
4.3 在GARCH-MIDAS模型下的股票市場波動 20
4.3.1 在已實現波動下的模型 22
4.3.2 總體經濟和經濟政策不確定性指標下的模型 23
第五章 實證結果分析 25
5.1 資料來源與處理 25
5.2 實證結果 25
5.2.1 單根檢定結果 26
5.2.2 具有已實現波動率的GARCH-MIDAS模型的選擇和估計 26
5.2.3 總體經濟變量和EPU估計GARCH-MIDAS模型 27
第六章 結論 37
參考文獻 39
附錄 43

中文書目
參考文獻
1.李慶男,(2022)“時間數列分析”講義 Chapter16，高雄，國立中山大學 經濟研究所.
2.黃裕烈·葉錦徽·陳重吉,(2021) “台灣經濟政策不確定性指標之建構與分 析”《經濟論文叢刊》，307–334.
英文書目
1.Alexopoulos, M., & Cohen, J. (2015). “The power of print: Uncertainty shocks, markets, and the economy.” International Review of Economics & Finance, 40, 8–28.
2.Arouri M., Estay C., Rault C., Roubaud D..(2016). “Economic policy uncertainty and stock markets: Long-run evidence from the US.” Finance Research Letter,18,136–141.
3.Bollerslev, T. (1986). “Generalized Autoregressive Conditional Heteroskedasticity.”Journal of Econometrics, 307–27.
4.Baker, S.R., Bloom, N., Davis, S.J., (2013). “Measuring Economic Policy Uncertainty. Chicago Booth Research Paper.” Chicago Booth Research Paper, 13–02.
5.Berna Karali, Gabriel J. Power, (2013). “Short-Run and Long-Run determinants of Commodity Price Volatility.” American Journal of Agricultural Economics, 723–738.
6.Baker, S.R., Bloom, N., Davis, S.J., (2016). “Measuring economic policy uncertainty.” Quart. J. Econ. 131, 1593–1636.
7.Campbell, John, (1991). “A Variance Decomposition for Stock Returns,” Economic Journal 101, 157–179.
8.Campbell, J., and R. Shiller., (1988). “The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors.” Review of Financial Studies 1,195–228.
9.Di Mo, R. Gupta, B. Li, T. Singh, (2018). “The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian market.” Economic Modelling,543–560.
10.Engle, Robert F. (1982).“Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica 50, 987–1008.
11.Engle, Robert F., and Gary G. J. Lee,(1999).“A Permanent and Transitory Component Model of Stock Return Volatility.” in Robert F. Engle and H. White (Eds.), Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger ,475–497.
12.Engle, Robert F., and Jose G. Rangel, (2008). “The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes,” Review of Financial Studies 21, 1187–1222.
13.Engle, R. F., Ghysels, E., & Sohn, B., (2013). “Stock market volatility and macroeconomic fundamentals.” The Review of Economics and Statistics, 95(3), 776–797.
14.Fama, Eugene F. and Kenneth R. French (1988a), "Permanent and Temporary Components of Stock Prices", Journal of Political Economy ,96:246-273.
15.Fama, Eugene F. and Kenneth R. French (1988b), "Dividend Yields and Expected Stock Returns", Journal of Financial Economics 22:3-25, October 1988.
16.Fama, Eugene F., and Kenneth R. French (1989), “Business Conditions and Expected Returns on Stock and Bonds,” Journal of Financial Economics 25 ,23–49.
17.Fang, L., Chen, B., Yu, H., & Qian, Y. ,(2018). “The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach.” Journal of Futures Markets, 38(3), 413–422.
18.Ghysels, Eric, Arthur Sinko, and Rossen Valkanov, (2006).“MIDAS Regressions: Further Results and New Directions,” Econometric Reviews, 53– 90.
19.Ghani Maria, Guo Qiang, Ma Feng, Li Tao.(2022). “Forecasting Pakistan stock market volatility: Evidence from economic variables and the uncertainty index.” International Review of Economics and Finance,1180–1189.
20.Jennifer Conrad and Gautam Kaul,(1988). “Time-Variation in Expected Returns.” The Journal of Business,409–425.
21.Karnizova, L., & Li, J. C. (2014). “Economic policy uncertainty, financial markets and probability of US recessions.”Economics Letters, 125(2), 261–265.
22.Liljeblom and Stenius. (1997). “Macroeconomic volatility and stock market volatility: Empirical evidence on Finnish data.” Applied Financial Economics,7(4),419–426.
23.Liu L., Zhang. T. (2015). “Economic policy uncertainty and stock market volatility.” Finance Research Letter,15,99–105.
24.Pastor and Veronesi ,(2012). “Uncertainty about Government Policy and Stock Prices.” The Journal of Finance, 1219-1264.
25.Pastor, L., & Veronesi, P. (2012). “Uncertainty about government policy and stock prices.” Journal of Finance, 68(4), 1219–1264.
Paye, B. S. (2012), “Predictive regressions for aggregate stock market volatility using macroeconomic variables.” Journal of Financial Economics, 527–546.
26.Robert C. Merton.(1980), “On estimating the expected return on the market: An exploratory investigation.” Journal of Financial Economics, 323-361.
27.Schwert, G. William, (1989).“Why Does Stock Market Volatility Change over Time?” Journal of Finance 44, 1207–1239.
28. Schwert, G. William, (1990). “Indexes of U.S. Stock Prices from 1802 to 1987.” Journal of Business 63, 399–426.