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研究生: 洪嘉宏
Hung, Chia-Hung
論文名稱: 貿易政策不確定性衝擊對美國、台灣股市的影響:混頻SVAR模型與反事實分析法之應用
The impact of trade policy uncertainty shocks on U.S. and Taiwan stock markets: Application of mixed-frequency SVAR model and counterfactual analysis
指導教授: 賴廷緯
Lai,Ting-Wei
口試委員: 蕭明福
Hsiao, Ming-Fu
吳易樺
Wu, Yi-Hua
學位類別: 碩士
Master
系所名稱: 社會科學學院 - 經濟學系
Department of Economics
論文出版年: 2026
畢業學年度: 114
語文別: 中文
論文頁數: 60
中文關鍵詞: 混合頻率結構向量自我迴歸模型貿易政策不確定性衝擊反事實分析匯率傳遞管道
外文關鍵詞: Mixed-frequency structural vector autoregression model, Trade policy uncertainty shocks, Counterfactual analysis, Exchange rate transmission channel
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  • 本研究旨在探討貿易政策不確定性(Trade Policy Uncertainty, TPU)衝擊,對美國與台灣股市指數之衝擊反應,以及利率與匯率於TPU衝擊中的傳導效果。研究運用2015年1月至2024年12月之數據,延伸Khalil and Strobel (2024) 與Boer, Menkhoff, and Rieth (2023) 之SVAR(Structural Vector Autoregression)模型,並導入Ferrara and Guérin (2018) 的混合資料頻率方法,建構堆疊式混合頻率SVAR模型。此外,本研究結合Kilian and Lewis (2011) 之反事實分析方法,旨在量化TPU衝擊下對兩國股市指數衝擊反應中利率與匯率傳遞管道之貢獻。
    實證結果發現TPU衝擊對兩國股市影響多數呈現短期上升或不顯著,且不同衝擊時點會使股價衝擊反應函數存在異質性。本研究發現,股市呈現短期正向反應主要由於近10年TPU指數普遍處在高水準且波動大於以往,市場面對TPU衝擊時的定價行為出現變化;而不同衝擊時點導致的異質性則主要源於總體經濟數據採樣窗口限制與各週次之TPU序列存在統計分佈差異。
    此外,反事實分析顯示兩國皆依賴於匯率管道傳遞衝擊,但兩國傳遞機制上存在差異。TPU衝擊下台灣股市主要由資本帳主導;美國股市則主要由經常帳主導。即在基準衝擊反應函數與反事實衝擊反應函數的偏離方向上,新台幣匯率與台股呈現同向對應關係(反映額外資本流入流出);美元匯率與美股則呈現反向對應關係(反映額外貿易競爭力增減)。本研究不僅深化既有文獻對TPU衝擊傳導機制之探討,亦為台灣應對外部不確定性提供了具體的政策參考方向。


    This study investigates the impulse responses of U.S. and Taiwan stock indices to Trade Policy Uncertainty (TPU) shocks, evaluating the transmission effects of interest and exchange rates. Using data from January 2015 to December 2024, we construct a stacked Mixed-Frequency SVAR model. Furthermore, we apply a counterfactual analysis framework to quantify the contributions of these transmission channels to stock market responses.
    Empirical results indicate that TPU shocks generally cause short-term positive or insignificant stock market reactions, with heterogeneity across different shock timings. The short-term positive response stems from the persistently elevated and volatile TPU levels over the past decade, which shifted market pricing behaviors. Meanwhile, the timing-induced heterogeneity is driven by macroeconomic data sampling constraints and statistical differences in weekly TPU distributions.
    Counterfactual analysis reveals both countries rely on the exchange rate channel, but with distinct mechanisms. Under TPU shocks, Taiwan's stock market is primarily capital account-driven, whereas the U.S. market is current account-driven. Specifically, regarding the deviation direction between baseline and counterfactual IRFs, the NTD exchange rate and Taiwan stock prices show a positive corresponding relationship (reflecting capital movements). Conversely, the USD exchange rate and U.S. stock prices exhibit an inverse corresponding relationship (reflecting trade competitiveness). These findings deepen the understanding of TPU transmission mechanisms and offer concrete policy insights for managing external uncertainties.

    第一章 緒論 1
    第二章 文獻回顧 7
    第一節 政策不確定性對總體經濟之影響 7
    第二節 貿易政策不確定性對資產價格之負面效果 7
    第三節 貿易政策不確定性對資產價格影響之複雜性 8
    第四節 本研究之延伸與貢獻 8
    第三章 模型設定 10
    第四章 實證結果 14
    第五章 反事實分析 20
    第一節 反事實分析之方法 20
    第二節 各傳導管道之反事實模擬結果 23
    第三節 虛擬衝擊序列之檢定與推論限制 31
    第六章 穩健性檢驗 32
    第一節 使用不同時間段的樣本與不同的滯後期數 32
    第二節 調換變數順序 35
    第三節 模型變數數量簡化 38
    第四節 裁剪COVID初期的異常樣本 39
    第五節 使用虛擬變數控制美國貨幣政策狀態 40
    第七章 實證結論 43
    第八章 政策意涵 46
    參考文獻 48
    附錄 51

    方文碩、田志遠(2001)。匯率貶值對股票市場的衝擊-雙變量GARCH-M模型。台灣金融財務季刊,2(3),99-117。
    徐清俊、李孟哲(2006)。匯率變動與台灣股市報酬之研究-雙變量GARCH模型。興國學報,(5),23-34。
    Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636.
    Bernanke, B. S., Gertler, M., Watson, M., Sims, C. A., & Friedman, B. M. (1997). Systematic Monetary Policy and the Effects of Oil Price Shocks. Brookings Papers on Economic Activity, 1997(1), 91–157.
    Bhattarai, S., Chatterjee, A., & Park, W. Y. (2020). Global spillover effects of US uncertainty. Journal of Monetary Economics, 114, 71–89.
    Bianconi, M., Esposito, F., & Sammon, M. (2021). Trade policy uncertainty and stock returns. Journal of International Money and Finance, 119, 102492.
    Bjørnland, H. C., & Jacobsen, D. H. (2010). The role of house prices in the monetary policy transmission mechanism in small open economies. Journal of Financial Stability, 6(4), 218–229.
    Boer, L., Menkhoff, L., & Rieth, M. (2023). The multifaceted impact of US trade policy on financial markets. Journal of Applied Econometrics, 38(3), 388–406.
    Caldara, D., Iacoviello, M., Molligo, P., Prestipino, A., & Raffo, A. (2020). The economic effects of trade policy uncertainty. Journal of Monetary Economics, 109, 38–59.
    Chiang, T. C. (2020). US policy uncertainty and stock returns: evidence in the US and its spillovers to the European Union, China and Japan. The Journal of Risk Finance, 21(5), 621–657.
    Ferrara, L., & Guérin, P. (2018). What are the macroeconomic effects of high-frequency uncertainty shocks? Journal of Applied Econometrics, 33(5), 662–679.
    Ghysels, E. (2016). Macroeconomics and the reality of mixed frequency data. Journal of Econometrics, 193(2), 294–314.
    Gozgor, G., Tiwari, A. K., Demir, E., & Akron, S. (2019). The relationship between Bitcoin returns and trade policy uncertainty. Finance Research Letters, 29, 75–82.
    He, F., Lucey, B., & Wang, Z. (2021). Trade policy uncertainty and its impact on the stock market -evidence from China-US trade conflict. Finance Research Letters, 40, 101753.
    Hoque, M. E., Soo-Wah, L., Uddin, M. A., & Rahman, A. (2023). International trade policy uncertainty spillover on stock market: Evidence from fragile five economies. The Journal of International Trade & Economic Development, 32(1), 104–131.
    Khalil, M., & Strobel, F. (2024). US trade policy and the US dollar. Journal of International Economics, 151, 103970.
    Kilian, L., & Lewis, L. T. (2011). Does the Fed Respond to Oil Price Shocks? The Economic Journal, 121(555), 1047–1072.
    Krippner, L. (2015). Zero Lower Bound Term Structure Modeling: A Practitioner’s Guide. Palgrave Macmillan.
    Kyriazis, N. A. (2021). Trade policy uncertainty effects on macro economy and financial markets: An integrated survey and empirical investigation. Journal of Risk and Financial Management, 14(1), 41.
    Leduc, S., & Liu, Z. (2016). Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics, 82, 20–35.
    Lenza, M., & Primiceri, G. E. (2022). How to estimate a vector autoregression after March 2020. Journal of Applied Econometrics, 37(4), 688–699.
    Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.
    Paccagnini, A., & Parla, F. (2021). Identifying high-frequency shocks with Bayesian mixed-frequency VARs. Available at SSRN 3855847.
    Parla, F. (2021). Financial Market Turbulence and Macro-Financial Developments in Ireland: A Mixed Data Sampling (MIDAS) Approach. Central Bank of Ireland Research Paper Series, No. 7.
    Schorfheide, F., & Song, D. (2024). Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic. International Journal of Central Banking, 20(4), 275–320.
    Sheikh, U. A., Asadi, M., Roubaud, D., & Hammoudeh, S. (2024). Global uncertainties and Australian financial markets: Quantile time-frequency connectedness. International Review of Financial Analysis, 92, 103098.
    Soenen, L. A., & Hennigar, E. S. (1988). An analysis of exchange rates and stock prices: the US experience between 1980 and 1986. Akron Business and Economic Review, 19(4), 7–16.
    Yilmazkuday, H. (2025). U.S. tariffs and stock prices. Finance Research Letters, 83, 107708.

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