2020年初COVID-19疫情爆發衝擊全球的經濟發展，臺灣雖不如全世界其他主要經濟體受創嚴重，但產業發展的情況與市場的運作模式也受到影響。本研究擬透過臺灣股市集中市場十一大產業指數的報酬率以及波動度，計算其外溢效果，探討在COVID-19疫情衝擊下，臺灣產業之間的連動性是否會有不一樣的變化。
本研究利用Diebold and Yilmaz (2012) 所提出的外溢指標，並對向量自我迴歸模型進行改善，使用2010年1月起到2022年5月十一大類股的日資料，採取Quandt-Andrews結構改變檢定與拔靴法尋找臨界值檢測模型裡的結構轉變點，再將報酬率模型資料與波動度模型資料劃分為多個子樣本，進一步分析疫情前後臺灣產業指數外溢效果的變化。
實證結果發現，在總外溢效果方面，臺灣產業指數報酬率在2020 年 12 月 7 日到 2022 年 5 月 31 日內，即在COVID-19 疫情的爆發後的期間，相對於上一個子樣本有所增加，至於波動度的總外溢效果則相對於其他子樣本有較大程度的增加，表示COVID-19疫情的爆發確實影響臺灣產業之間的連動性。其中在報酬率方面，半導體業在疫情爆發後影響其他產業程度減少，但仍為主要影響者，而光電業及油電燃氣業則是在疫情爆發後影響其他產業的程度大幅增加；波動度方面，電腦及周邊設備業在疫情前後都是主要影響者，半導體業在疫情爆發後受其他產業波動度的影響增加較多，光電業在疫情前為被影響者的角色，在疫情爆發後則成為影響其他產業指數波動度的角色。

This paper aims to compare the difference in the interdependence of industries in Taiwan under the COVID-19 epidemic by calculating spillover effects of the return and volatility of the eleven industry indices in the concentrated market of the Taiwan stock market. The spillover index is proposed by Diebold and Yilmaz (2012) using the daily data of eleven major stocks from January 2010 to May 2022. The vector autoregression model is improved by considering exogenous variables and adopts the Quandt-Andrews structural change test and bootstrapping method to find the structural change points. The data are divided into subsamples with breakpoints for discussion and further comparison of the spillover effects of industrial index before and after the COVID-19 epidemic. The results suggests that the total spillover effect of the returns model did not increase significantly after the COVID-19 epidemic, while the total spillover effect of volatility model increased to a greater extent compared to other subsamples. In terms of individual industries, the returns of semiconductor industry influenced less on other industries after the epidemic, but it remained the major influencer, while the returns of optoelectronics industry and oil and gas industries were significantly influenced by other industries after the epidemic. The volatility of optoelectronics industry was an influencer before the epidemic, but it was influenced by other industries instead after the epidemic.

Andrews, Donald W. K. (1993). “Tests for Parameter Instability and Structural Change with Unknown Change point,” Econometrica, 61, 821–856.
Andrews, Donald W. K. (2003a). “Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum,” Econometrica,71, 391–397.
Andrews, Donald W. K. Andrews (2003b). “End-of-sample Instability Tests,” Econometrica ,71, 1661–1694.
Bai, Jushan, Robin L. Lumsdaine and James H. Stock (1998). “Testing for and Dating Common Breaks in Multivariate Time Series,” The Review of Economic Studies, 64, 395–432.
Bai, Jushan and Pierre Perron (1998). “Estimating and Testing Linear Models
with Multiple Structural Changes,” Econometrica, 66, 47–78.
Baruník, Jozef, Evžen Kočenda and Lukáš Vácha (2015). “Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers,” Journal of Financial Markets, 27, 55–78.
Boeckelmann, Lukas and Arthur Stalla-Bourdillon (2020). “Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission,” Working Paper.
Diebold, Francis X. and Kamil Yilmaz (2009). “Measuring Financial Asset
Return and Volatility Spillovers, with Application to Global Equity,” Economic Journal, 119, 158–171.
El-Shazly, Alaa (2016). “Structural Breaks and Monetary Dynamics: A Time Series Analysis,” Economic Modelling, 53(C), 133-143.

Koop, Gary, M. Hashem Pesaran and Simon M. Potter (1996). “Impulse Response Analysis in Non-linear Multivariate Models,” Journal of Econometrics, 74,119–147.
Parkinson, Michael(1980). “The Extreme Value Method for Estimating the Variance of the Rate of Return,” Journal of Business, 53, 61–65.
Pesaran, H. Hashem and Yongcheol Shin (1998). “Generalized Impulse Response Analysis in Linear Multivariate Models,” Economics Letters, 58, 17–29.
Quandt, Richard E. (1960). “Test of the Hypothesis that a Linear Regression Obeys Two Separate Regimes,” Journal of the American Statistical Association, 55, 324-330.
Palanska, Tereza (2018). “Measurement of Volatility Spillovers and Asymmetric Connectedness on Commodity and Equity Markets,” Working Paper.
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)，407-442。
陳冠潔、張興華 (2019)。美國量化寬鬆政策對台灣匯率市場之外溢效果。中國統計學報 (Journal of the Chinese Statistical Association)，57，71–86。