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題名 台灣金融不確定性指標之建構與應用
Construction and Application of Taiwan’s Financial Uncertainty Index
作者 宋孟璇
Sung, Meng-Hsuan
貢獻者 林馨怡<br>顏佑銘
Lin, Hsin-Yi<br>Yen, Yu-Min
宋孟璇
Sung, Meng-Hsuan
關鍵詞 金融不確定性指標
總體經濟不確定性指標
JLN方法
主成分分析
GARCH模型
VAR模型
Financial uncertainty
Macroeconomic uncertainty
JLN methodology
PCA
GARCH
VAR
日期 2025
上傳時間 4-Aug-2025 12:51:34 (UTC+8)
摘要 本研究採用Jurado et al. (2015) 與 Ludvigson et al. (2021) 所提出之方法，建構適用於台灣市場的總體經濟不確定性指標與金融不確定性指標，並分析這些指標與台灣實體經濟活動的關係。研究蒐集2004年1月至2024年10月期間的118項總體經濟變數與52項金融變數，前者來自林馨怡教授建置的總體經濟與貨幣金融大數據資料庫，後者則取自臺灣證券交易所與臺灣經濟新報資料庫。 不確定性被定義為經濟主體無法準確預測未來狀況的條件性波動，本研究透過計算預測誤差變異數來衡量不確定性。研究方法首先採用主成分分析（PCA）從眾多變數中提取主要因子，降低資料維度；接著結合共同因子建立動態迴歸模型，計算預測誤差；再使用GARCH(1,1)-t模型描述預測誤差的波動特性；最後將各變數的不確定性衡量值進行加總，形成整體指標。 實證結果顯示，本研究指標能有效反映2008年金融危機、2012年歐債危機、2015年中國股災以及2020年COVID-19疫情等重大事件。台灣金融不確定性指標與美國對應指標呈現高度正相關。台灣總體經濟不確定性指標與美國對應指標亦具有中偏高度正相關，但波動幅度相對較小。靜態與動態相關性分析均顯示，金融不確定性指標與工業生產成長率呈現顯著負相關。向量自我迴歸（VAR）模型進一步證實，前一期金融不確定性對當期工業生產呈現顯著負向影響。穩健性測試包括將波動率指標（VIX）納入模型作為外生變數、以VIX替代金融不確定性指標，以及子樣本分析，所有測試均支持上述發現。
This study constructs macroeconomic and financial uncertainty indices for Taiwan by applying the methodologies of Jurado et al. (2015) and Ludvigson et al. (2021) and examines their relationship with Taiwan's real economic activity. The dataset comprises 118 macroeconomic variables and 52 financial variables spanning January 2004 – October 2024, collected from Professor Hsin-Yi Lin's Macroeconomic Database, the Taiwan Stock Exchange (TWSE), and TEJ. Uncertainty is defined as the conditional volatility of forecast errors, reflecting the inability of economic agents to predict future conditions accurately. The empirical framework employs Principal Component Analysis (PCA) to extract factors and reduce dimensionality, builds dynamic regression models with the common factors, applies GARCH(1,1)-t models to capture volatility characteristics, and aggregates the individual measures into comprehensive indices. The results indicate that the financial uncertainty index effectively captures major events—including the 2008 global financial crisis, 2012 European debt crisis, 2015 Chinese stock crash, and 2020 COVID-19 pandemic. Taiwan's financial uncertainty index is highly correlated with its U.S. counterpart, whereas the macroeconomic uncertainty index shows a moderate-to-high correlation but lower volatility. Both static and dynamic analyses reveal significant negative correlations between financial uncertainty and industrial-production growth. Vector Autoregression (VAR) models further confirm that lagged financial uncertainty exerts a negative effect on current industrial production. Robustness checks—incorporating the VIX as an exogenous variable, substituting VIX for the domestic index, and conducting subsample analyses—reinforce these findings.
參考文獻 陶芷敏（2023），〈台灣大型總體經濟研究資料庫及應用〉，國立政治大學經濟學系碩士論文。 Adrian, T., and Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705–1741. Adrian, T., and Shin, H. S. (2010). Liquidity and leverage. Journal of Financial Intermediation, 19(3), 418–437. Alessandri, P., and Mumtaz, H. (2019). Financial regimes and uncertainty shocks. Journal of Monetary Economics, 101, 31–46. Allen, F., and Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1–33. Anderson, E. W., Ghysels, E., and Juergens, J. L. (2009). The impact of risk and uncertainty on expected returns. Journal of Financial Economics, 94(2), 233–263. Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2006). The cross-section of volatility and expected returns. The Journal of Finance, 61(1), 259–299. Asgharian, H., Christiansen, C., and Hou, A. J. (2023). The effect of uncertainty on stock market volatility and correlation. Journal of Banking & Finance, 154, 106929. Bai, J., and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221. Baker, S. R., Bloom, N., and Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. Bekaert, G., Hoerova, M., and Lo Duca, M. (2013). Risk, uncertainty and monetary policy. Journal of Monetary Economics, 60(7), 771–788. Bekaert, G., Engstrom, E. C., and Xu, N. R. (2022). The time variation in risk appetite and uncertainty. Management Science, 68(6), 3975–4004. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547. Bollerslev, T., Todorov, V., and Xu, L. (2015). Tail risk premia and return predictability. Journal of Financial Economics, 118(1), 113–134. Brunnermeier, M. K., and Pedersen, L. H. (2009). Market liquidity and funding liquidity. The Review of Financial Studies, 22(6), 2201–2238. Campbell, J. Y., Giglio, S., Polk, C., and Turley, R. (2018). An intertemporal CAPM with stochastic volatility. Journal of Financial Economics, 128(2), 207–233. Carr, P., and Wu, L. (2009). Variance risk premiums. The Review of Financial Studies, 22(3), 1311–1341. Cesa-Bianchi, A., Pesaran, M. H., and Rebucci, A. (2020). Uncertainty and economic activity: A multicountry perspective. The Review of Financial Studies, 33(8), 3393–3445. Danielsson, J., Shin, H. S., and Zigrand, J. P. (2012). Endogenous and systemic risk. In Quantifying Systemic Risk (pp. 73–94). University of Chicago Press. Danielsson, J., Valenzuela, M., and Zer, I. (2018). Learning from history: Volatility and financial crises. The Review of Financial Studies, 31(7), 2774–2805. Diebold, F. X., and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. Diether, K. B., Malloy, C. J., and Scherbina, A. (2002). Differences of opinion and the cross section of stock returns. The Journal of Finance, 57(5), 2113–2141. Dungey, M., Fry, R., González-Hermosillo, B., and Martin, V. L. (2010). Transmission of financial crises and contagion: A latent factor approach. Oxford University Press. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441. Jiang, Y., Liu, X., and Tse, C. K. (2024). Financial uncertainty and stock market volatility. European Financial Management, 28(4), 833–860. Jolliffe, I. T. (2002). Principal component analysis. Springer. Jurado, K., Ludvigson, S. C., and Ng, S. (2015). Measuring uncertainty. American Economic Review, 105(3), 1177–1216. Larsen, V. H. (2021). Components of uncertainty. International Economic Review, 62(2), 769–788. Leduc, S., and Liu, Z. (2016). Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics, 82, 20–35. Longstaff, F. A. (2010). The subprime credit crisis and contagion in financial markets. Journal of Financial Economics, 97(3), 436–450. Ludvigson, S. C., Ma, S., and Ng, S. (2021). Uncertainty and business cycles: Exogenous impulse or endogenous response? American Economic Journal: Macroeconomics, 13(4), 369–410. Ludvigson, S. C., Ng, S., and Ma, S. (2017). Shock restricted structural vector-autoregressions. (NBER Working Paper No. w23225). Mankiw, N. G., Reis, R., and Wolfers, J. (2003). Disagreement about inflation expectations. NBER Macroeconomics Annual, 18, 209–248. Pastor, L., and Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219–1264. Pastor, L., and Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. Patton, A. J., and Timmermann, A. (2010). Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion. Journal of Monetary Economics, 57(7), 803–820. Stock, J. H., and Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179. Van Nieuwerburgh, S., and Veldkamp, L. (2010). Information acquisition and under-diversification. The Review of Economic Studies, 77(2), 779–805.
描述 碩士
國立政治大學
經濟學系
112258022
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112258022
資料類型 thesis
dc.contributor.advisor 林馨怡<br>顏佑銘zh_TW
dc.contributor.advisor Lin, Hsin-Yi<br>Yen, Yu-Minen_US
dc.contributor.author (Authors) 宋孟璇zh_TW
dc.contributor.author (Authors) Sung, Meng-Hsuanen_US
dc.creator (作者) 宋孟璇zh_TW
dc.creator (作者) Sung, Meng-Hsuanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 12:51:34 (UTC+8)-
dc.date.available 4-Aug-2025 12:51:34 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 12:51:34 (UTC+8)-
dc.identifier (Other Identifiers) G0112258022en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158279-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 112258022zh_TW
dc.description.abstract (摘要) 本研究採用Jurado et al. (2015) 與 Ludvigson et al. (2021) 所提出之方法，建構適用於台灣市場的總體經濟不確定性指標與金融不確定性指標，並分析這些指標與台灣實體經濟活動的關係。研究蒐集2004年1月至2024年10月期間的118項總體經濟變數與52項金融變數，前者來自林馨怡教授建置的總體經濟與貨幣金融大數據資料庫，後者則取自臺灣證券交易所與臺灣經濟新報資料庫。 不確定性被定義為經濟主體無法準確預測未來狀況的條件性波動，本研究透過計算預測誤差變異數來衡量不確定性。研究方法首先採用主成分分析（PCA）從眾多變數中提取主要因子，降低資料維度；接著結合共同因子建立動態迴歸模型，計算預測誤差；再使用GARCH(1,1)-t模型描述預測誤差的波動特性；最後將各變數的不確定性衡量值進行加總，形成整體指標。 實證結果顯示，本研究指標能有效反映2008年金融危機、2012年歐債危機、2015年中國股災以及2020年COVID-19疫情等重大事件。台灣金融不確定性指標與美國對應指標呈現高度正相關。台灣總體經濟不確定性指標與美國對應指標亦具有中偏高度正相關，但波動幅度相對較小。靜態與動態相關性分析均顯示，金融不確定性指標與工業生產成長率呈現顯著負相關。向量自我迴歸（VAR）模型進一步證實，前一期金融不確定性對當期工業生產呈現顯著負向影響。穩健性測試包括將波動率指標（VIX）納入模型作為外生變數、以VIX替代金融不確定性指標，以及子樣本分析，所有測試均支持上述發現。zh_TW
dc.description.abstract (摘要) This study constructs macroeconomic and financial uncertainty indices for Taiwan by applying the methodologies of Jurado et al. (2015) and Ludvigson et al. (2021) and examines their relationship with Taiwan's real economic activity. The dataset comprises 118 macroeconomic variables and 52 financial variables spanning January 2004 – October 2024, collected from Professor Hsin-Yi Lin's Macroeconomic Database, the Taiwan Stock Exchange (TWSE), and TEJ. Uncertainty is defined as the conditional volatility of forecast errors, reflecting the inability of economic agents to predict future conditions accurately. The empirical framework employs Principal Component Analysis (PCA) to extract factors and reduce dimensionality, builds dynamic regression models with the common factors, applies GARCH(1,1)-t models to capture volatility characteristics, and aggregates the individual measures into comprehensive indices. The results indicate that the financial uncertainty index effectively captures major events—including the 2008 global financial crisis, 2012 European debt crisis, 2015 Chinese stock crash, and 2020 COVID-19 pandemic. Taiwan's financial uncertainty index is highly correlated with its U.S. counterpart, whereas the macroeconomic uncertainty index shows a moderate-to-high correlation but lower volatility. Both static and dynamic analyses reveal significant negative correlations between financial uncertainty and industrial-production growth. Vector Autoregression (VAR) models further confirm that lagged financial uncertainty exerts a negative effect on current industrial production. Robustness checks—incorporating the VIX as an exogenous variable, substituting VIX for the domestic index, and conducting subsample analyses—reinforce these findings.en_US
dc.description.tableofcontents 致謝 i 中文摘要 ii 英文摘要 iii 表目錄 vi 圖目錄 vii 1 緒論 1 2 文獻回顧 3  2.1 不確定性的衡量方法 3  2.2 金融不確定性對市場的影響 4 3 台灣不確定性指標的建構 7  3.1 台灣總體經濟與金融數據集 7  3.2 金融不確定性指標之建構方法 10  3.3 總體經濟不確定性指標建構方法 15 4 台灣不確定性指標之建構結果 18  4.1 台灣金融不確定性指標 18  4.2 台灣總體經濟不確定性指標 19 5 台灣不確定性指標之應用 22  5.1 相關性分析 22  5.2 VAR 模型 26  5.3 穩健性測試 28 6 結論 33 參考文獻 35 A 金融變數列表 38zh_TW
dc.format.extent 1490630 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112258022en_US
dc.subject (關鍵詞) 金融不確定性指標zh_TW
dc.subject (關鍵詞) 總體經濟不確定性指標zh_TW
dc.subject (關鍵詞) JLN方法zh_TW
dc.subject (關鍵詞) 主成分分析zh_TW
dc.subject (關鍵詞) GARCH模型zh_TW
dc.subject (關鍵詞) VAR模型zh_TW
dc.subject (關鍵詞) Financial uncertaintyen_US
dc.subject (關鍵詞) Macroeconomic uncertaintyen_US
dc.subject (關鍵詞) JLN methodologyen_US
dc.subject (關鍵詞) PCAen_US
dc.subject (關鍵詞) GARCHen_US
dc.subject (關鍵詞) VARen_US
dc.title (題名) 台灣金融不確定性指標之建構與應用zh_TW
dc.title (題名) Construction and Application of Taiwan’s Financial Uncertainty Indexen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 陶芷敏（2023），〈台灣大型總體經濟研究資料庫及應用〉，國立政治大學經濟學系碩士論文。 Adrian, T., and Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705–1741. Adrian, T., and Shin, H. S. (2010). Liquidity and leverage. Journal of Financial Intermediation, 19(3), 418–437. Alessandri, P., and Mumtaz, H. (2019). Financial regimes and uncertainty shocks. Journal of Monetary Economics, 101, 31–46. Allen, F., and Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1–33. Anderson, E. W., Ghysels, E., and Juergens, J. L. (2009). The impact of risk and uncertainty on expected returns. Journal of Financial Economics, 94(2), 233–263. Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2006). The cross-section of volatility and expected returns. The Journal of Finance, 61(1), 259–299. Asgharian, H., Christiansen, C., and Hou, A. J. (2023). The effect of uncertainty on stock market volatility and correlation. Journal of Banking & Finance, 154, 106929. Bai, J., and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221. Baker, S. R., Bloom, N., and Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. Bekaert, G., Hoerova, M., and Lo Duca, M. (2013). Risk, uncertainty and monetary policy. Journal of Monetary Economics, 60(7), 771–788. Bekaert, G., Engstrom, E. C., and Xu, N. R. (2022). The time variation in risk appetite and uncertainty. Management Science, 68(6), 3975–4004. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547. Bollerslev, T., Todorov, V., and Xu, L. (2015). Tail risk premia and return predictability. Journal of Financial Economics, 118(1), 113–134. Brunnermeier, M. K., and Pedersen, L. H. (2009). Market liquidity and funding liquidity. The Review of Financial Studies, 22(6), 2201–2238. Campbell, J. Y., Giglio, S., Polk, C., and Turley, R. (2018). An intertemporal CAPM with stochastic volatility. Journal of Financial Economics, 128(2), 207–233. Carr, P., and Wu, L. (2009). Variance risk premiums. The Review of Financial Studies, 22(3), 1311–1341. Cesa-Bianchi, A., Pesaran, M. H., and Rebucci, A. (2020). Uncertainty and economic activity: A multicountry perspective. The Review of Financial Studies, 33(8), 3393–3445. Danielsson, J., Shin, H. S., and Zigrand, J. P. (2012). Endogenous and systemic risk. In Quantifying Systemic Risk (pp. 73–94). University of Chicago Press. Danielsson, J., Valenzuela, M., and Zer, I. (2018). Learning from history: Volatility and financial crises. The Review of Financial Studies, 31(7), 2774–2805. Diebold, F. X., and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. Diether, K. B., Malloy, C. J., and Scherbina, A. (2002). Differences of opinion and the cross section of stock returns. The Journal of Finance, 57(5), 2113–2141. Dungey, M., Fry, R., González-Hermosillo, B., and Martin, V. L. (2010). Transmission of financial crises and contagion: A latent factor approach. Oxford University Press. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441. Jiang, Y., Liu, X., and Tse, C. K. (2024). Financial uncertainty and stock market volatility. European Financial Management, 28(4), 833–860. Jolliffe, I. T. (2002). Principal component analysis. Springer. Jurado, K., Ludvigson, S. C., and Ng, S. (2015). Measuring uncertainty. American Economic Review, 105(3), 1177–1216. Larsen, V. H. (2021). Components of uncertainty. International Economic Review, 62(2), 769–788. Leduc, S., and Liu, Z. (2016). Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics, 82, 20–35. Longstaff, F. A. (2010). The subprime credit crisis and contagion in financial markets. Journal of Financial Economics, 97(3), 436–450. Ludvigson, S. C., Ma, S., and Ng, S. (2021). Uncertainty and business cycles: Exogenous impulse or endogenous response? American Economic Journal: Macroeconomics, 13(4), 369–410. Ludvigson, S. C., Ng, S., and Ma, S. (2017). Shock restricted structural vector-autoregressions. (NBER Working Paper No. w23225). Mankiw, N. G., Reis, R., and Wolfers, J. (2003). Disagreement about inflation expectations. NBER Macroeconomics Annual, 18, 209–248. Pastor, L., and Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219–1264. Pastor, L., and Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. Patton, A. J., and Timmermann, A. (2010). Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion. Journal of Monetary Economics, 57(7), 803–820. Stock, J. H., and Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179. Van Nieuwerburgh, S., and Veldkamp, L. (2010). Information acquisition and under-diversification. The Review of Economic Studies, 77(2), 779–805.zh_TW