English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109874/140825 (78%)
Visitors : 45940152      Online Users : 148
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146471

    Title: 臺灣產業類股間因果關係之研究:以雙重變數選擇過程檢驗高維度 Granger 因果關係
    Causal Relationship between Sector Indices in Taiwan Stock Market:Testing High-Dimensional Granger Causality with a Post-Double-Selection Procedure
    Authors: 劉芳均
    Liou, Fang-Jun
    Contributors: 徐士勛
    Hsu, Shih-Hsun
    Liou, Fang-Jun
    Keywords: 產業類股報酬關係
    Sector Indices Relationship
    HD-Granger Causality
    Post Double Selection Procedure
    Edge Betweenness
    Date: 2023
    Issue Date: 2023-08-02 13:41:26 (UTC+8)
    Abstract: 本研究採用 COVID-19 疫情期間的資料探討 27 個台股產業指數週報酬間的因果關係,並分別探討美國聯準會實施量化寬鬆和量化緊縮政策兩段期間資金面的變化對產業股價報酬關係的影響。本文主要根據 Hecq et al. (2019) 提出的分析架構,透過 LASSO 方法進行雙重變數選擇對原高維度 VAR 模型進行系統降維,以得到更為穩健的估計與推論,最後再利用網路圖呈現 Granger 因果關係和分群結果。


    綜合分析上述兩段期間的分群結果,在顯著水準為 1% 之下,均將網路分成多群大小相異的族群;在顯著水準為 5% 之下,均將較關聯的產業分成一個大族群,以及其餘的獨立族群,兩種類型的結果分別適合關注小群類股以及整體產業趨勢的研究者。
    This research aims to analyze the causal relationship between the weekly returns of 27 stock sector indices in Taiwan stock market during the COVID-19 epidemic period by testing high-dimensional Granger causality with a post-double-selection procedure. The analysis framework is primarily based on the methodology proposed by Hecq et al. (2019), and the findings are presented through network graphs.

    Our findings indicate that during the period of quantitative easing, the returns of biotechnology and medical, food and information services sectors primarily Granger caused the returns of other industries, suggesting that these sectors were potential leading stocks at that time. On the other hand, during the period of quantitative tightening, our findings indicate that the returns of construction and financial sectors primarily Granger caused the returns of other industries. However, the estimated coefficients showed positive or negative values, indicating varied performances across sectors.

    Fianaly, the clustering results indicate under significance levels of 1% and 5%, respectively, the former consistently partitioned the network into multiple communities of varying sizes. In contrast, the latter resulted in a single large community and the remaining independent communities.
    Reference: 徐士勛與李佳磬 (2019),「亞洲主要股市報酬波動的潛在鏈結程度衡量」,《經濟論文叢刊》,47(4), 579-620。

    郭維裕,李淯靖,陳致綱與林建秀 (2015),「臺灣產業指數的外溢效果」,《經濟論文叢刊》,43(4), 407-442。

    黃裕烈與管中閔 (2014),《向量自我迴歸模型,計量方法與 R 程式》,台北: 雙葉書廊。

    鄒易憑與白東岳 (2009),「原油價格與原油產業指數之動態關係: 厚尾跳躍模型之應用」,《臺灣金融財務季刊》,10(3), 87-111。

    蔡坤旻 (2009),《原油價格變動對於太陽能產業指數的影響-雙門檻 GARCH 模型之應用》,臺北大學國際企業研究所學位論文。

    Belloni, A., Chernozhukov, V., and Hansen, C. (2014), “High-dimensional methods and inference on structural and treatment effects,” Journal of Economic Perspectives, 28(2), 29-50.

    Belloni, A., Chernozhukov, V., and Hansen, C. (2014), “Inference on treatment effects after selection among high dimensional controls,” The Review of Economic Studies, 81(2), 608-650.

    Cao, D., Long, W., and Yang, W. (2014), “Sector indices correlation analysis in China’s stock market,” Procedia Computer Science, 17, 1241-1249.

    Cavaliere, G., D. I. Harvey, S. J. Leybourne, and A. R. Taylor (2011), “Testing for unit roots in the presence of a possible break in trend and nonstationary volatility,” Econometric Theory, 27(5), 957-991.

    Chen, Y., Li, W., and Qu, F. (2019), “Dynamic asymmetric spillovers and volatility interdependence on China’s stock market,” Physica A: Statistical Mechanics and its Applications, 523, 825-838.

    Corsi, F., Lillo, F., Pirino, D., and Trapin, L. (2018), “Measuring the propagation of financial distress with Granger-causality tail risk networks,” Journal of Financial Stability, 38, 18-36.

    Granger, C. W. (1980), “Testing for causality: A personal viewpoint,” Journal of Economic Dynamics and control, 2, 329-352.

    Hao, J., and He, F. (2018), “Univariate dependence among sectors in Chinese stock market and systemic risk implication,” Physica A: Statistical Mechanics and its Applications, 510, 355-364.

    Hecq, A., Margaritella, L., and Smeekes, S. (2019), “Granger causality testing in high-dimensional VARs: a post-double-selection procedure,” arXiv preprint arXiv:1902.10991.

    Hecq, A., Margaritella, L., and Smeekes, S. (2023), “Inference in Non-stationary High-Dimensional VARs,” arXiv preprint arXiv:2302.01434.

    Laopodis, N. T. (2016), “Industry returns, market returns and economic fundamentals: Evidence for the United States,” Economic Modelling, 53, 89-106.

    Leeb, H., and Pötscher, B. M. (2005), “Model selection and inference: Facts and fiction,” Econometric Theory, 21(1), 21-59.

    Newman, M. E., and Girvan, M. (2004), “Finding and evaluating community structure in networks,” Physical review E, 69(2), 026113.

    Shahzad, S. J. H., Naeem, M. A., Peng, Z., and Bouri, E. (2022), “Asymmetric volatility spillover among Chinese sectors during COVID-19,” International Review of Financial Analysis, 75, 101754.

    Shirokikh, O., Pastukhov, G., Semenov, A., Butenko, S., Veremyev, A., Pasiliao, E. L., and Boginski, V. (2022), “Networks of causal relationships in the US stock market,” Dependence Modeling, 10(1), 177-190.

    Song, X., and Taamouti, A. (2019), “A better understanding of granger causality analysis: A big data environment,” Oxford Bulletin of Economics and Statistics, 81(4), 911-936.

    Tibshirani, R. (1996), “Regression shrinkage and selection via the lasso” Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.

    Toda, H. Y., and Yamamoto, T. (1995), “Statistical inference in vector autoregressions with possibly integrated processes,” Journal of econometrics, 66(1-2), 225-250.

    Výrost, T., Lyócsa, Š., and Baumöhl, E. (2015), “Granger causality stock market networks: Temporal proximity and preferential attachment” Physica A: Statistical Mechanics and its Applications, 427, 262-276.

    Wilms, I., S. Gelper, and C. Croux. (2016), “The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach” European Journal of Operational Research, 254(1), 138-147.

    Zhou, X., Zhang, H., Zheng, S., Xing, W., Yang, H., and Zhao, Y. (2023), “A study on the transmission of trade behavior of global nickel products from the perspective of the industrial chain,” Resources Policy, 81, 103376.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110258004
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    800401.pdf1869KbAdobe PDF20View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    著作權政策宣告 Copyright Announcement
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback