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題名 探討中國半導體供應鏈上中下游股價之波動外溢效果
Exploring the volatility spillover effects among the stock price of the Upper, Middle and Lower Stream of Semiconductor Industry in China作者 杜婉瓊
Du, Wan-Qiong貢獻者 林靖<br>李慧琳
Lin, Jin<br>Lee, Huey-Lin
杜婉瓊
Du, Wan-Qiong關鍵詞 GARCH-MIDAS模型
中美科技戰
外溢效果
中國半導體產業
股價波動
GARCH-MIDAS model
US-China scientific and technological war
spillover effect
Chinese semiconductor industry
stock price fluctuation日期 2021 上傳時間 4-Aug-2021 16:01:22 (UTC+8) 摘要 自中美科技戰爆發以來，美國制裁了包括華為和中芯國際在內的多家中國半導體公司以阻止其在該產業的發展，對中國半導體產業產生巨大影響。中國政府也採取多項政策以扶持半導體產業的國產化。因應經濟事件的發生和一系列政策的出台，中國半導體產業股票發生巨大波動。本研究以該研究背景出發，欲從產業的角度探討中國半導體產業鏈（上游對中游、中游對下游）股價波動關係。本研究運用GARCH-MIDAS模型實證分析2016年11月1日至2021年3月25日中國半導體產業鏈之外溢效果。全樣本期間涵蓋中美科技戰與COVID-19兩個事件。本研究蒐集八家IC設計公司、兩家晶圓代工公司以及三家封裝測試廠共十三家半導體上市公司股票收盤價數據，並根據公司年度報表的「業務概要」裡公司目前能實現的工藝技術依據半導體製程構建中國半導體供應鏈。根據GARCH-MIDAS模型實證結果顯示，在中美科技戰期間，大部分IC設計公司的已實現波動會對中游晶圓代工公司產生長期正向的波動外溢效果；中游晶圓代工廠的低頻報酬率波動會對下游封裝測試廠商產生負向的波動外溢效果。上述證實了半導體垂直供應鏈之間存在股價波動外溢效果，因此當投資人在做投資組合時，應考慮到半導體產業投資標的之間的長期波動關係，以便進行相應套利和避險的舉措。
Since the outbreak of the US-China scientific and technological war, the United States has imposed sanctions on several Chinese semiconductor companies, including Huawei and SMIC, preventing their development in the industry. It has had a huge impact on the Chinese semiconductor industry. Due to the occurrence of US-China scientific and technological war and a series of Chinese government’s policies, the stock price of China`s semiconductor industry fluctuated greatly. The purpose of this paper is to explore the fluctuation among various segments of China`s semiconductor industry chain (upstream to midstream and midstream to downstream) from the perspective of industry. This study collected the data from November 1, 2016 to March 25, 2021 and applied GARCH-MIDAS model to analyze the spillover effects of semiconductor industry chain. In this study, we collect the closing price of 13 listed Semiconductor Companies from eight IC design companies, two wafer foundry companies and three Assembly and testing companies, and build a Chinese semiconductor supply chain based on the current technology that the companies can be achieved in the annual report of the company. Based on the GARCH-MIDAS model, the empirical results show that most of the IC design companies’ fluctuations will have significantly positive volatility spillover effects on wafer foundry companies in the long term during the US-China scientific and technological war. Low-frequency fluctuations in the Assembly and testing companies will have significantly negative fluctuation spillover effects on Assembly and testing companies in the long term. The results confirm the spillover effect of stock price fluctuations within vertical semiconductor supply chains. Therefore, when making a portfolio, investors should consider the long-term fluctuation relationship among different links of semiconductor industry for the purpose of making appropriate arbitrage and hedge measures.參考文獻 Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH‐MIDAS approach.Journal of Forecasting,32(7), 600-612.Ayers, J. B. (2001). Supply chain strategies. In Making Supply Chain ManagementWork(pp. 125-136). Auerbach Publications.Black, F. (1976). Studies of stock market volatility changes. Proceedings of the American statistical association business and economic statistics section.Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics,31(3), 307-327.Bown, C. P. (2020). How the United States marched the semiconductor industry into its trade war with China. East Asian Economic Review, 24(4), 349-388.Chen, T. L., Cheng, C. H., & Teoh, H. J. (2007). Fuzzy time-series based on Fibonacci sequence for stock price forecasting. Physica A: Statistical Mechanics and its Applications, 380, 377-390.Chou, T. L., Chang, J. Y., & Li, T. C. (2014). Government support, FDI clustering and semiconductor sustainability in china: Case studies of Shanghai, Suzhou and Wuxi in the Yangtze delta. Sustainability, 6(9), 5655-5681.Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.Conrad, C., & Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models.Journal of Applied Econometrics, 35(1), 19-45.Delnavaz, B., & Fallah Shams, M. (2019). Studying Volatility Risk Transmission in Automatable Supply Chain Companies in the Tehran Stock Exchange. International Journal of Finance & Managerial Accounting, 3(12), 29-37.Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, 48(5), 1749-1778.Feng, S., Li, H., Qi, Y., Jia, J., Zhou, G., Guan, Q., & Liu, X. (2019). Detecting the interactions among firms in distinct links of the industry chain by motif. Journal of Statistical Mechanics: Theory and Experiment, 2019(12), 123403.Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance,48(5), 1779-1801.Grimes, S., & Du, D. (2020). China`s emerging role in the global semiconductor value chain. Telecommunications Policy, 101959.Hendricks, K. B., Jacobs, B. W., & Singhal, V. R. (2020). Stock market reaction to supply chain disruptions from the 2011 Great East Japan Earthquake. Manufacturing & Service Operations Management, 22(4), 683-699.Huang, C. Y., & Lin, P. K. (2014). Application of integrated data mining techniques in stock market forecasting. Cogent Economics & Finance, 2(1), 929505.Kim, H. M., & O’Connor, K. (2018). Foreign direct investment flows and urban dynamics in a developing country: a case study of Korean activities in Suzhou, China. International Planning Studies.Nieh, C. C., Shao-Bin, L., & Chuang, H. M. (2005). A study on the interrelationships among the stock indexes of the upper, middle and lower stream of semiconductor industry in Taiwan. Tai Da Guan Li Lun Cong, 15(2), 25.Pan, W., Zhao, H., & Miu, L. (2019). An empirical study on supply chain risk contagion effect based on VAR-GARCH (1, 1)–BEKK model. Wireless Personal Communications, 109(2), 761-775.Pan, Z., Wang, Y., Wu, C., & Yin, L. (2017). Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model. Journal of Empirical Finance,43, 130-142.VerWey, J. (2019). Chinese semiconductor industrial policy: Past and present.J. Int`l Com. & Econ., 1.VerWey, J. (2019). Chinese semiconductor industrial policy: prospects for future success. J. Int`l Com. & Econ., 1.Wang, C. H., & Chen, J. Y. (2019). Demand forecasting and financial estimation considering the interactive dynamics of semiconductor supply-chain companies. Computers & Industrial Engineering, 138, 106104.Wang, C. T., & Chiu, C. S. (2014). Competitive strategies for Taiwan`s semiconductor industry in a new world economy. Technology in Society, 36, 60-73.Wei, Y., Yu, Q., Liu, J., & Cao, Y. (2018). Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model. Physica A: Statistical Mechanics and Its Applications, 492, 923-930.Wu, S. Q., Tsao, C. C., Chang, P. C., Fan, C. Y., Chen, M. H., & Zhang, X. (2017, July). A study of patent analysis for stock price prediction. In 2017 4th International Conference on Information Science and Control Engineering (ICISCE) 115-119. IEEE.Xu, Q., Bo, Z., Jiang, C., & Liu, Y. (2019). Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility. Knowledge-Based Systems, 166, 170-185.Yang, C., & Hung, S. W. (2003). Taiwan`s dilemma across the Strait: lifting the ban on semiconductor investment in China. Asian Survey,43(4), 681-696.Yinug, F. (2009). Challenges to foreign investment in high-tech semiconductor production in China. J. Int`l Com. & Econ.,2, 97.Zhang, Y. J., & Wang, J. L. (2019). Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models. Energy Economics,78, 192-201.Zhou, X., Chen, H., Chai, J., Wang, S., & Lev, B. (2020). Performance evaluation and prediction of the integrated circuit industry in China: A hybrid method. Socio-Economic Planning Sciences, 69, 100712.劉祥熹, & 劉浩宇. (2012). 台灣 TFT-LCD 產業上中下游股價之長期記憶, 關聯性與波動外溢效果之研究: FIEC-HYGARCH 模型之應用.應用經濟論叢, (92), 119-162. 描述 碩士
國立政治大學
經濟學系
108258048資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108258048 資料類型 thesis dc.contributor.advisor 林靖<br>李慧琳 zh_TW dc.contributor.advisor Lin, Jin<br>Lee, Huey-Lin en_US dc.contributor.author (Authors) 杜婉瓊 zh_TW dc.contributor.author (Authors) Du, Wan-Qiong en_US dc.creator (作者) 杜婉瓊 zh_TW dc.creator (作者) Du, Wan-Qiong en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 16:01:22 (UTC+8) - dc.date.available 4-Aug-2021 16:01:22 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 16:01:22 (UTC+8) - dc.identifier (Other Identifiers) G0108258048 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136571 - 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) 108258048 zh_TW dc.description.abstract (摘要) 自中美科技戰爆發以來，美國制裁了包括華為和中芯國際在內的多家中國半導體公司以阻止其在該產業的發展，對中國半導體產業產生巨大影響。中國政府也採取多項政策以扶持半導體產業的國產化。因應經濟事件的發生和一系列政策的出台，中國半導體產業股票發生巨大波動。本研究以該研究背景出發，欲從產業的角度探討中國半導體產業鏈（上游對中游、中游對下游）股價波動關係。本研究運用GARCH-MIDAS模型實證分析2016年11月1日至2021年3月25日中國半導體產業鏈之外溢效果。全樣本期間涵蓋中美科技戰與COVID-19兩個事件。本研究蒐集八家IC設計公司、兩家晶圓代工公司以及三家封裝測試廠共十三家半導體上市公司股票收盤價數據，並根據公司年度報表的「業務概要」裡公司目前能實現的工藝技術依據半導體製程構建中國半導體供應鏈。根據GARCH-MIDAS模型實證結果顯示，在中美科技戰期間，大部分IC設計公司的已實現波動會對中游晶圓代工公司產生長期正向的波動外溢效果；中游晶圓代工廠的低頻報酬率波動會對下游封裝測試廠商產生負向的波動外溢效果。上述證實了半導體垂直供應鏈之間存在股價波動外溢效果，因此當投資人在做投資組合時，應考慮到半導體產業投資標的之間的長期波動關係，以便進行相應套利和避險的舉措。 zh_TW dc.description.abstract (摘要) Since the outbreak of the US-China scientific and technological war, the United States has imposed sanctions on several Chinese semiconductor companies, including Huawei and SMIC, preventing their development in the industry. It has had a huge impact on the Chinese semiconductor industry. Due to the occurrence of US-China scientific and technological war and a series of Chinese government’s policies, the stock price of China`s semiconductor industry fluctuated greatly. The purpose of this paper is to explore the fluctuation among various segments of China`s semiconductor industry chain (upstream to midstream and midstream to downstream) from the perspective of industry. This study collected the data from November 1, 2016 to March 25, 2021 and applied GARCH-MIDAS model to analyze the spillover effects of semiconductor industry chain. In this study, we collect the closing price of 13 listed Semiconductor Companies from eight IC design companies, two wafer foundry companies and three Assembly and testing companies, and build a Chinese semiconductor supply chain based on the current technology that the companies can be achieved in the annual report of the company. Based on the GARCH-MIDAS model, the empirical results show that most of the IC design companies’ fluctuations will have significantly positive volatility spillover effects on wafer foundry companies in the long term during the US-China scientific and technological war. Low-frequency fluctuations in the Assembly and testing companies will have significantly negative fluctuation spillover effects on Assembly and testing companies in the long term. The results confirm the spillover effect of stock price fluctuations within vertical semiconductor supply chains. Therefore, when making a portfolio, investors should consider the long-term fluctuation relationship among different links of semiconductor industry for the purpose of making appropriate arbitrage and hedge measures. en_US dc.description.tableofcontents 謝辭 ⅰ摘要 ⅱAbstract ⅲ目錄 ⅴ表次 ⅵ圖次 ⅷ第一章 緒論 1第一節 研究背景與動機 1第二節 文獻缺口 3第三節 研究目的 4第四節 研究框架 5第二章 文獻回顧 6第一節 半導體產業相關文獻 6第二節 供應鏈相關文獻 7第三節 GARCH-MIDAS相關文獻 10第三章 研究方法 13第一節 樣本數據蒐集 13第二節 研究設計 16第三節 GARCH-MIDAS實證模型 19第四節 研究假說 20第四章 實證結果 27第一節 資料描述與敘述性統計 27第二節 定態分析與條件異質變異檢定 29第三節 GARCH-MIDAS模型實證結果——全樣本時期 32第四節 GARCH-MIDAS模型實證結果——子樣本時期 40第五章 結論與建議 55第一節 研究發現與經濟意涵 55第二節 研究限制與建議 57參考文獻 58附錄1 61附錄2 62 zh_TW dc.format.extent 3269552 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108258048 en_US dc.subject (關鍵詞) GARCH-MIDAS模型 zh_TW dc.subject (關鍵詞) 中美科技戰 zh_TW dc.subject (關鍵詞) 外溢效果 zh_TW dc.subject (關鍵詞) 中國半導體產業 zh_TW dc.subject (關鍵詞) 股價波動 zh_TW dc.subject (關鍵詞) GARCH-MIDAS model en_US dc.subject (關鍵詞) US-China scientific and technological war en_US dc.subject (關鍵詞) spillover effect en_US dc.subject (關鍵詞) Chinese semiconductor industry en_US dc.subject (關鍵詞) stock price fluctuation en_US dc.title (題名) 探討中國半導體供應鏈上中下游股價之波動外溢效果 zh_TW dc.title (題名) Exploring the volatility spillover effects among the stock price of the Upper, Middle and Lower Stream of Semiconductor Industry in China en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH‐MIDAS approach.Journal of Forecasting,32(7), 600-612.Ayers, J. B. (2001). Supply chain strategies. In Making Supply Chain ManagementWork(pp. 125-136). Auerbach Publications.Black, F. (1976). Studies of stock market volatility changes. Proceedings of the American statistical association business and economic statistics section.Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics,31(3), 307-327.Bown, C. P. (2020). How the United States marched the semiconductor industry into its trade war with China. East Asian Economic Review, 24(4), 349-388.Chen, T. L., Cheng, C. H., & Teoh, H. J. (2007). Fuzzy time-series based on Fibonacci sequence for stock price forecasting. Physica A: Statistical Mechanics and its Applications, 380, 377-390.Chou, T. L., Chang, J. Y., & Li, T. C. (2014). Government support, FDI clustering and semiconductor sustainability in china: Case studies of Shanghai, Suzhou and Wuxi in the Yangtze delta. Sustainability, 6(9), 5655-5681.Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.Conrad, C., & Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models.Journal of Applied Econometrics, 35(1), 19-45.Delnavaz, B., & Fallah Shams, M. (2019). Studying Volatility Risk Transmission in Automatable Supply Chain Companies in the Tehran Stock Exchange. International Journal of Finance & Managerial Accounting, 3(12), 29-37.Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, 48(5), 1749-1778.Feng, S., Li, H., Qi, Y., Jia, J., Zhou, G., Guan, Q., & Liu, X. (2019). Detecting the interactions among firms in distinct links of the industry chain by motif. Journal of Statistical Mechanics: Theory and Experiment, 2019(12), 123403.Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance,48(5), 1779-1801.Grimes, S., & Du, D. (2020). China`s emerging role in the global semiconductor value chain. Telecommunications Policy, 101959.Hendricks, K. B., Jacobs, B. W., & Singhal, V. R. (2020). Stock market reaction to supply chain disruptions from the 2011 Great East Japan Earthquake. Manufacturing & Service Operations Management, 22(4), 683-699.Huang, C. Y., & Lin, P. K. (2014). Application of integrated data mining techniques in stock market forecasting. Cogent Economics & Finance, 2(1), 929505.Kim, H. M., & O’Connor, K. (2018). Foreign direct investment flows and urban dynamics in a developing country: a case study of Korean activities in Suzhou, China. International Planning Studies.Nieh, C. C., Shao-Bin, L., & Chuang, H. M. (2005). A study on the interrelationships among the stock indexes of the upper, middle and lower stream of semiconductor industry in Taiwan. Tai Da Guan Li Lun Cong, 15(2), 25.Pan, W., Zhao, H., & Miu, L. (2019). An empirical study on supply chain risk contagion effect based on VAR-GARCH (1, 1)–BEKK model. Wireless Personal Communications, 109(2), 761-775.Pan, Z., Wang, Y., Wu, C., & Yin, L. (2017). Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model. Journal of Empirical Finance,43, 130-142.VerWey, J. (2019). Chinese semiconductor industrial policy: Past and present.J. Int`l Com. & Econ., 1.VerWey, J. (2019). Chinese semiconductor industrial policy: prospects for future success. J. Int`l Com. & Econ., 1.Wang, C. H., & Chen, J. Y. (2019). Demand forecasting and financial estimation considering the interactive dynamics of semiconductor supply-chain companies. Computers & Industrial Engineering, 138, 106104.Wang, C. T., & Chiu, C. S. (2014). Competitive strategies for Taiwan`s semiconductor industry in a new world economy. Technology in Society, 36, 60-73.Wei, Y., Yu, Q., Liu, J., & Cao, Y. (2018). Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model. Physica A: Statistical Mechanics and Its Applications, 492, 923-930.Wu, S. Q., Tsao, C. C., Chang, P. C., Fan, C. Y., Chen, M. H., & Zhang, X. (2017, July). A study of patent analysis for stock price prediction. In 2017 4th International Conference on Information Science and Control Engineering (ICISCE) 115-119. IEEE.Xu, Q., Bo, Z., Jiang, C., & Liu, Y. (2019). Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility. Knowledge-Based Systems, 166, 170-185.Yang, C., & Hung, S. W. (2003). Taiwan`s dilemma across the Strait: lifting the ban on semiconductor investment in China. Asian Survey,43(4), 681-696.Yinug, F. (2009). Challenges to foreign investment in high-tech semiconductor production in China. J. Int`l Com. & Econ.,2, 97.Zhang, Y. J., & Wang, J. L. (2019). Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models. Energy Economics,78, 192-201.Zhou, X., Chen, H., Chai, J., Wang, S., & Lev, B. (2020). Performance evaluation and prediction of the integrated circuit industry in China: A hybrid method. Socio-Economic Planning Sciences, 69, 100712.劉祥熹, & 劉浩宇. (2012). 台灣 TFT-LCD 產業上中下游股價之長期記憶, 關聯性與波動外溢效果之研究: FIEC-HYGARCH 模型之應用.應用經濟論叢, (92), 119-162. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101157 en_US
