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研究生:龔家緯
研究生(外文):GONG, JIA-WEI
論文名稱:美國利率期限結構如何對台灣股市之影響:以0050、0056為例
論文名稱(外文):The Impact of the U.S. Term Structure of Interest Rate on the Taiwan Stock Market: A Case Study of 0050 and 0056
指導教授:李修全李修全引用關係
指導教授(外文):LEE, HSIU-CHUAN
口試委員:李修全王譯賢簡正儀
口試委員(外文):LEE, HSIU-CHUANWANG, YI-HSIENCHIEN, CHENG-YI
口試日期:2024-01-12
學位類別:碩士
校院名稱:銘傳大學
系所名稱:財務金融學系碩士在職專班
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:33
中文關鍵詞:美國利率期限結構外溢效果
外文關鍵詞:The U.S. term structure of interest rateSpillover effects
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本研究探討美國利率期限結構對臺灣50指數ETF(0050.TW)及臺灣高股息指數ETF(0056.TW)的關係,採用 2013年1月至2022年12月的日資料。使用Diebold與Yilmaz (2009,2012)提出的向量自我迴歸模型(VAR)來衡量外溢效果,計算出總外溢效果(total spillovers)、定向外溢效果(directional spillovers)及淨外溢效果(net spillovers),並對總外溢效果和淨外溢效果中具有重要傳遞作用的金融市場進行了因子分析。利用Diebold and Li於(2006)提出將Nelson and Siegel模型的利率長期水準(Level)、斜率(Slope)及曲率(Curvature)三個主成分參數改為隨著時間呈現動態變化,即為DNS模型,雖同樣沿用了 Nelson-Siegel 模型的架構,但 Diebold-Li 模型在估計上並非以非線性最小平方法(nonlinear least squares)進行參數之估計。本研究使用了Guirreri(2015)所撰寫的「Yield Curve」R套件進行研究。與Diebold and Li論文中描述的方法不同,在這個方程式中,參數 λ_t 不是固定的,而是在每個時間步驟 t中進行估計。具體來說,在每一次預測步驟中,對於每個到期日,我們迭代地估計一個參數λ,使其β_3的負載最大化。然後,通過OLS方法估計這個 λ 的所有β值。然後,從這些β值中選擇使估計模型在所有到期期限上殘差最小的參數集。
研究結果發現:利率長期水準(Level),對臺灣50指數ETF(0050.TW)影響較大,斜率(Slope),對臺灣50指數ETF(0050.TW)影響較大,曲率(Curvature),對臺灣高股息指數ETF(0056.TW)影響較大,疫情前,利率期限結構Level、Slope、Curvature對臺灣50指數ETF(0050.TW)及臺灣高股息指數ETF(0056.TW)影響都不是非常明顯,而在疫情後利率期限結構Level、Slope、Curvature對臺灣50指數ETF(0050.TW)及臺灣高股息指數ETF(0056.TW)影響變得非常明顯,本研究中顯示不論COVID19前後,臺灣50指數ETF(0050.TW)及臺灣高股息指數ETF(0056.TW)對其他指數的影響都是非常明顯的,顯示兩者在台灣金融領域扮演著重要的角色。
This study investigates the relationship between the U.S. term structure of interest rate and Taiwan's 50 Index ETF (0050.TW) and Taiwan High Dividend Index ETF (0056.TW), using daily data from January 2013 to December 2022. The Vector Autoregression Model (VAR) proposed by Diebold and Yilmaz (2009, 2012) is employed to measure spillover effects, calculating total spillovers, directional spillovers, and net spillovers. The study also conducts factor analysis on financial markets that play a significant role in total and net spillover effects.
The Diebold and Li model (2006) dynamically modifies the Nelson and Siegel model's interest rate long-term level, slope, and curvature parameters, diverging from the Nelson-Siegel model's framework by not estimating parameters using nonlinear least squares. This research utilized the 'YieldCurve' R package by Guirreri (2015). Unlike the method described in the Diebold and Li paper, in this equation, the parameter λ_t is not fixed but estimated at each time step t. Specifically, for each maturity date, a parameter λ is iteratively estimated in each forecasting step to maximize the loading of β_3. Then, all β values for this λ are estimated using the OLS method, and the parameter set that minimizes residuals across all maturities in the estimated model is selected.
The findings show that the long-term Level has a significant impact on Taiwan's 50 Index ETF (0050.TW), the Slope primarily affects Taiwan's 50 Index ETF (0050.TW), and the Curvature significantly influences the Taiwan High Dividend Index ETF (0056.TW). Before the pandemic, the effects of Level, Slope, and Curvature on both 0050.TW and 0056.TW were not very evident. However, after the pandemic, these factors' impact on both ETFs became much more pronounced. The study indicates that regardless of the COVID-19 situation, both 0050.TW and 0056.TW have a significant influence on other indices, highlighting their important role in Taiwan's financial domain.
表目錄 ...................................................................................................... VI
圖目錄 ..................................................................................................... VII
第壹章 緒論............................................................................................... 1
第一節 研究背景 ................................................................................ 1
第二節 研究動機與目的.................................................................... 2
第三節 研究架構 ................................................................................ 3
第貳章 文獻回顧 ...................................................................................... 5
第一節、資產外溢 (spillover) 與市場連動 ..................................... 5
第二節、利率期限結構與向量自我迴歸模型之建模 ..................... 7
第三節 文獻評論 .............................................................................. 11
第參章 研究方法 .................................................................................... 12
第一節 資料來源 .............................................................................. 12
第二節 利率期限模型之建構 ......................................................... 13
第三節 實證迴歸模型 ...................................................................... 14
第肆章 實證結果 .................................................................................... 16
第一節 基本統計量 .......................................................................... 16
第二節 利率期限結構與股價報酬外溢和市場影響 ..................... 18
第三節 利率期限結構與Covid-19疫情股價波動與外溢影響 .... 20
第伍章 結論............................................................................................. 22
參考文獻 ................................................................................................... 23
一、中文文獻: ................................................................................ 23
二、英文文獻: ................................................................................ 24
一、中文文獻:
1.王冠閔、黃柏農(2004)。台灣股、匯市與美國股市關聯性探討。臺灣經濟預測與政策,第34卷第2期,頁31-72。
2.林容如、蔡麗茹、張哲晟(2017)。股票市場與金屬、原油市場間的報酬連動與波動外溢效果之研究。管理實務與理論研究,第11第1期,頁19-48。
3.周建新、于鴻福、張千雲(2009)。利率期限結構變動與債券型基金投資績效。臺大管理論叢,第20第1期,頁189-225。
4.周建新、于鴻福、張千雲、楊孟波(2003)。利率期限結構變動與公債投資組合免疫策略。企業管理學報第59期,頁97-122。
5.周建新、于鴻福、劉嘉烜(2007)。利率期限結構估計模型與公債交易策略。中山管理評論,第15第4期,頁779-815。
6.胡德榮(2004)。台灣公債市場利率期限結構之估計。國立高雄第一科技大學財務管理系碩士論文。
7.郭維裕、李淯靖、陳致綱、林建秀(2015)。台灣產業指數的外溢效果。經濟論文叢刊,43:4,頁407-442。
8.彭開琼、林翠蓉、張淑儀(2015)。金融危機對台灣ETF投資績效的影響:以歐債危機為例。華人經濟研究,第13卷第1期。
9.葉仕國、林丙輝(2002)。以主成份分析方法計算台灣利率期限結構的風險值。台灣管理學刊,第1第2期,頁275-288。 

二、英文文獻:
1.Bredin, D., O'Sullivan, C., Spencer, S., 2021. Forecasting WTI crude oil futures returns: Does the term structure help? Energy Economics 100, 105350.
2.Czaja M.G., Scholz H., 2006. Sensitivity of stock returns to changes in the term structure of interest rates — Evidence from the German market, Operations Research Proceedings 2006, 305–310.
3.Diebold, F.X., Li, C., 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130, 337-364.
4.Diebold, F.X., Yilmaz, K., 2009. Measuring financial asset return and volatility spillovers, with application to global equity. Economic Journal 119, 158-171.
5.Diebold, F.X., Yilmaz, K., 2012, Better to give than to receive: Predictive directional measurement of volatility spillover, International Journal of Forecasting 28, 57–66.
6.Gabauer, D., Subramaniam, S., Gupta, R., 2020. On the transmission mechanism of Asia-Pacific yield curve characteristics. International Journal of Finance and Economics 27, 473-488.
7.Guirreri, S. (2015). YieldCurve: Modelling and estimation of the yield curve. R package version 4.1. https://CRAN.r-project.org/package=YieldCurve.
8.Hammoudeh, S., Malik, F., McAleer, M., 2011. Risk management of precious metals. Quarterly Review of Economics and Finance 51, 435–441.
9.Hammoudeh, S., Yuan, Y., 2008. Metal volatility in presence of oil and interest rate shocks. Energy Economics 30, 606–620.
10.Hammoudeh, S., Yuan, Y., McAleer, M., Thompson, M.A., 2010. Precious metals–exchange rate volatility transmissions and hedging strategies. International Review of Economics and Finance 19, 633–647.
11.Nelson, C. R., Siegel, A. F., 1987. Parsimonious modeling of yield curves. Journal of Business 60, 473-489.
12.Svensson, L. E., 1994. Estimating and interpreting forward interest rates: Sweden 1992-1994 (No. w4871). National Bureau of Economic Research.
13.Sowmya, S., Prasanna K., Bhaduri S., 2016. Linkages in the term structure of interest rates across sovereign bond markets. Emerging Markets Review 27, 118-139.
14.Tully, E., Lucey, B.M., 2007. A power garch examination of the gold market. Research in International Business and Finance 21, 316–325.
15.Umar, Z., Riaz, Y., Aharon, D.Y., 2022. Network connectedness dynamics of the yield curve of G7 countries. International Review of Economics and Finance 79, 275-288.
16.Umar, Z., Riaz, Y., Shahab, Y., Teplova, T., 2023. Network connectedness of the term structure of yield curve and global Sukuks. Pacific-Basin Finance Journal 80, 102056.
17.Umar, Z., Yousaf, I., Aharon, D.Y., 2021. The relationship between yield curve components and equity sectorial indices: Evidence from China. Pacific-Basin Finance Journal 68, 101591.
18.Umar, Z., Yousaf, I., Gubareva, M., Vo, X.V., 2022. Spillover and risk transmission between the term structure of the US interest rates and Islamic equities. Pacific-Basin Finance Journal 72, 101712.
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