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題名 以小波方法分析台灣股市價量關係及類股輪動變化
Estimating the Relationship between Price and Trading Volume and Sector Rotation in Taiwan Stock Market by using Wavelet approaches作者 戴妙珊
Tai, Miao-Shan貢獻者 徐士勛
戴妙珊
Tai, Miao-Shan關鍵詞 價量關係
類股輪動
小波相干性
時頻域因果關係
Price-volume relationship
Sector rotation
Wavelet coherence
Time-frequency domain causality日期 2022 上傳時間 1-Jul-2022 17:09:43 (UTC+8) 摘要 本研究採用時頻域架構下的小波方法來研究台灣股市的價量關係，並與時域架構下之Granger 因果關係檢定結果比較其異同。另外，本研究更進一步利用小波方法來探討台灣股市中是否存在類股輪動的現象。我們選定六個標的進行分析，分別是台灣加權股價指數及五個產業指數。在價量關係上，轉換為定態後，時域架構下的分析皆呈現一致的領先落後關係；但時頻域架構下，小波相干性除放寬序列為定態的假設外，其實證結果顯示同一個標的之價格與成交量的連動性及因果關係會隨不同的時間及頻率而有所改變。而透過各產業指數價格與加權指數價格的領先落後關係分析，我們也發現台灣股市中確實有產業類股輪動的現象。
This study uses the wavelet approaches in the time-frequency domain to study the price-volume relationship of the Taiwan stock market, and compares the similarities and differences with the results of the Granger causality test in the time-domain. In addition, this study further uses the wavelet approaches to explore whether there is a sector rotation in the Taiwan stock market.We select six targets for analysis, namely the Taiwan Capitalization Weighted Stock Index and five industry indices. In terms of price-volume relationship, after converting the series to be time-domain, the analysis shows a consistent lead-lag relationship. However, in the time-frequency domain, in addition to relaxing the assumption that the series are stationary, the empirical results of wavelet coherence show that the co-movement and causality between the price and trading volume of the same target varies with different times and frequencies. Through the analysis of the lead-lag relationship between the prices of various industry indices and the Taiwan Capitalization Weighted Stock Index price, we also found that there is indeed a sector rotation in the Taiwan stock market.參考文獻 林思如，陳宗仁，王憲斌與魏石勇(2017)，「股市規模波動的價量關係—以台灣股票市場為例」，《中華管理評論國際學報》， 20(2)。莊家彰與管中閔(2005)，「台灣與美國股市價量關係的分量迴歸分析」，《經濟論文》， 33(4)， 379-404。劉映興與陳家彬(2002)，「台灣股票市場交易值、交易量與發行量加權股價指數關係之實證研究—光譜分析之應用」，《農業經濟半年刊》， 72，65-87。Aluko, O.A., and P.O. Adeyeye (2020), “Imports and economic growth in Africa: testing for Granger causality in the frequency domain,” The Journal of International Trade & Economic Development, 29(7), 850-864.Bahmani-Oskooee, M., Chang, T., and Ranjbar, O. (2016),“Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches,” Economic Modelling, 56, 66-78.Bojanic, A.N. (2012), “The impact of financial development and trade on the economic growth of Bolivia,” Journal of Applied Economics, 15(1), 51-70.Chen, S.W. (2008), “Untangling the nexus of stock price and trading volume: evidence from the Chinese stock market,” Economics Bulletin, 7(15), 1-16.Croes, R., and Rivera, M.A. (2010), “Testing the empirical link between tourism and competitiveness: evidence from Puerto Rico,” Tourism Economics, 16(1), 217-234.Croux, C., and Reusens, P. (2013), “Do stock prices contain predictive power for the future economic activity? A Granger causality analysis in the frequency domain,” Journal of Macroeconomics, 35, 93-103.Dickey, D.A., and W.A. Fuller (1979), “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, 74, 427-431.Goffe, W. (1994), “Wavelets in macroeconomics: an introduction,” Computational techniques for econometrics and economic analysis, 137-149.Graham, M., and Nikkinen, J. (2011), “Co-movement of the Finnish and international stock markets: a wavelet analysis.,” The European Journal of Finance, 17:5-6, 409-425.Granger, C.W.J. (1969), “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society, 37, 424-438.Grinsted, A., Moore, J.C., and Jevrejeva, S. (2004), “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process Geophysics, 11, 561-566.Gronwald, M. (2009), “Reconsidering the macroeconomics of the oil price in Germany: testing for causality in the frequency domain,” Empirical Economics, 36, 441-453.Gupta, S., Das, D., Hasim, H., and Tiwari, A.K. (2018), “The dynamic relationship between stock returns and trading volume revisited: a MODWTVAR approach,” Finance Research Letters, 27, 91-98.Hudgins, L., Friehe, C., and Mayer, M. (1993), “Wavelet transforms and atmospheric turbulence,” Physical Review Letters, 71, 3279–3282.Hui, E.C.M., and Yue S. (2006), “Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study,” Journal of Real Estate Finance and Economics, 33, 299-327.Jain, P.C., and Joh, G.-H. (1988), “The dependence between hourly prices and trading volume,” The Journal of Financial and Quantitative Analysis, 23(3), 269-283.Jumbe, C.B.L. (2004), “Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi,” Energy Economics, 26, 61-68.Kirikkaleli, D., and Güngör, H. (2021), “Comovement of commodity price indexes and energy price index: a wavelet coherence approach,” Financial Innovation, 7:15.Lee, B.-S., and Rui, O.M. (2002), “The dynamic relationship between stock returns and trading volume: domestic and cross-country evidence,” Journal of Banking & Finance, 26, 51-78.Li, X.L., Chang, T., Miller, S., Balcilar, M., and Gupta, R. (2015), “The comovement and causality between the U.S. housing and stock markets in the time and frequency domains,” International Review of Economics and Finance, 38, 220-233.Loh, L. (2013), “Co-movement of Asia-Pacific with European and US stock market returns: a cross-time-frequency analysis,” Research in International Business and Finance, 29, 1-13.Pal, D., and Mitra, S.K. (2017), “Time-frequency contained co-movement of crude oil and world food prices: a wavelet-based analysis,” Energy Economics, 62, 230-239.Pinzón, K. (2018), “Dynamics between energy consumption and economic growth in Ecuador: a granger causality analysis,” Economic Analysis and Policy, 57, 88-101.Rahman, M.M., and Kashem, M.A. (2017), “Carbon emissions, energy consumption and industrial growth in Bangladesh: empirical evidence from ARDL cointegration and Granger causality analysis,” Energy Policy, 110, 600-608.Ramsey, J.B., and Zhang, Z. (1996), “The analysis of foreign exchange data using waveform dictionaries,” Journal of Empirical Finance, 4, 341-372.Reboredo, J.C., and Rivera-Castro, M.A. (2014), “Wavelet based evidence of the impact of oil prices on stock returns,” International Review of Economics & Finance, 29, 145-176.Rua, A., and Nunes, L.C. (2009), “International comovement of stock market returns: a wavelet analysis,” Journal of Empirical Finance, 12, 632-639.Said E., and Dickey, D.A. (1984), “Testing for unit roots in autoregressive moving average models of unknown order,” Biometrika, 71, 599-607.Tiwari, A. K., M. I. Mutascu, C. T. Albulescu, and P. Kyophilavong (2015), “Frequency domain causality analysis of stock market and economic activity in India,” International Review of Economics and Finance, 39, 224-238.Toda, H. Y., and T. Yamamoto (1995), “Statistical inference in vector autoregressions with possibly integrated process,” Journal of Econometrics, 66(1-2), 225-250.Yilanci, V., Ozgur, O., and Gorus, M.S. (2021), “Stock prices and economic activity nexus in OECD countries: new evidence from an asymmetric panel Granger causality test in the frequency domain,” Financial Innovation, 7:11. 描述 碩士
國立政治大學
經濟學系
109258010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109258010 資料類型 thesis dc.contributor.advisor 徐士勛 zh_TW dc.contributor.author (Authors) 戴妙珊 zh_TW dc.contributor.author (Authors) Tai, Miao-Shan en_US dc.creator (作者) 戴妙珊 zh_TW dc.creator (作者) Tai, Miao-Shan en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Jul-2022 17:09:43 (UTC+8) - dc.date.available 1-Jul-2022 17:09:43 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2022 17:09:43 (UTC+8) - dc.identifier (Other Identifiers) G0109258010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140756 - 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) 109258010 zh_TW dc.description.abstract (摘要) 本研究採用時頻域架構下的小波方法來研究台灣股市的價量關係，並與時域架構下之Granger 因果關係檢定結果比較其異同。另外，本研究更進一步利用小波方法來探討台灣股市中是否存在類股輪動的現象。我們選定六個標的進行分析，分別是台灣加權股價指數及五個產業指數。在價量關係上，轉換為定態後，時域架構下的分析皆呈現一致的領先落後關係；但時頻域架構下，小波相干性除放寬序列為定態的假設外，其實證結果顯示同一個標的之價格與成交量的連動性及因果關係會隨不同的時間及頻率而有所改變。而透過各產業指數價格與加權指數價格的領先落後關係分析，我們也發現台灣股市中確實有產業類股輪動的現象。 zh_TW dc.description.abstract (摘要) This study uses the wavelet approaches in the time-frequency domain to study the price-volume relationship of the Taiwan stock market, and compares the similarities and differences with the results of the Granger causality test in the time-domain. In addition, this study further uses the wavelet approaches to explore whether there is a sector rotation in the Taiwan stock market.We select six targets for analysis, namely the Taiwan Capitalization Weighted Stock Index and five industry indices. In terms of price-volume relationship, after converting the series to be time-domain, the analysis shows a consistent lead-lag relationship. However, in the time-frequency domain, in addition to relaxing the assumption that the series are stationary, the empirical results of wavelet coherence show that the co-movement and causality between the price and trading volume of the same target varies with different times and frequencies. Through the analysis of the lead-lag relationship between the prices of various industry indices and the Taiwan Capitalization Weighted Stock Index price, we also found that there is indeed a sector rotation in the Taiwan stock market. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究架構 2第二章 文獻回顧 2第三章 研究方法 8第一節 時頻域架構下之因果關係分析方法 8第一小節 連續小波轉換 8第二小節 小波功率頻譜 10第三小節 小波相干性 11第四小節 小波相位差 11第二節 時域架構下之因果關係分析方法 12第一小節 單根檢定 13第二小節 Granger因果關係檢定 14第四章 資料 14第一節 變數選取 15第二節 資料來源 16第三節 資料說明 17第五章 實證結果 25第一節 時域架構下之因果關係 25第二節 時頻域架構下之因果關係 28第一小節 價格對成交量之月資料 28第二小節 價格對成交量之日資料 31第三小節 各產業指數價格與加權股價指數之領先落後關係 38第六章 結論 45第七章 參考文獻 47附錄 52A 價格與成交量之敘述統計 52B 價格與成交量之小波功率頻譜圖 55 zh_TW dc.format.extent 9843321 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109258010 en_US dc.subject (關鍵詞) 價量關係 zh_TW dc.subject (關鍵詞) 類股輪動 zh_TW dc.subject (關鍵詞) 小波相干性 zh_TW dc.subject (關鍵詞) 時頻域因果關係 zh_TW dc.subject (關鍵詞) Price-volume relationship en_US dc.subject (關鍵詞) Sector rotation en_US dc.subject (關鍵詞) Wavelet coherence en_US dc.subject (關鍵詞) Time-frequency domain causality en_US dc.title (題名) 以小波方法分析台灣股市價量關係及類股輪動變化 zh_TW dc.title (題名) Estimating the Relationship between Price and Trading Volume and Sector Rotation in Taiwan Stock Market by using Wavelet approaches en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 林思如，陳宗仁，王憲斌與魏石勇(2017)，「股市規模波動的價量關係—以台灣股票市場為例」，《中華管理評論國際學報》， 20(2)。莊家彰與管中閔(2005)，「台灣與美國股市價量關係的分量迴歸分析」，《經濟論文》， 33(4)， 379-404。劉映興與陳家彬(2002)，「台灣股票市場交易值、交易量與發行量加權股價指數關係之實證研究—光譜分析之應用」，《農業經濟半年刊》， 72，65-87。Aluko, O.A., and P.O. Adeyeye (2020), “Imports and economic growth in Africa: testing for Granger causality in the frequency domain,” The Journal of International Trade & Economic Development, 29(7), 850-864.Bahmani-Oskooee, M., Chang, T., and Ranjbar, O. (2016),“Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches,” Economic Modelling, 56, 66-78.Bojanic, A.N. (2012), “The impact of financial development and trade on the economic growth of Bolivia,” Journal of Applied Economics, 15(1), 51-70.Chen, S.W. (2008), “Untangling the nexus of stock price and trading volume: evidence from the Chinese stock market,” Economics Bulletin, 7(15), 1-16.Croes, R., and Rivera, M.A. (2010), “Testing the empirical link between tourism and competitiveness: evidence from Puerto Rico,” Tourism Economics, 16(1), 217-234.Croux, C., and Reusens, P. (2013), “Do stock prices contain predictive power for the future economic activity? A Granger causality analysis in the frequency domain,” Journal of Macroeconomics, 35, 93-103.Dickey, D.A., and W.A. Fuller (1979), “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, 74, 427-431.Goffe, W. (1994), “Wavelets in macroeconomics: an introduction,” Computational techniques for econometrics and economic analysis, 137-149.Graham, M., and Nikkinen, J. (2011), “Co-movement of the Finnish and international stock markets: a wavelet analysis.,” The European Journal of Finance, 17:5-6, 409-425.Granger, C.W.J. (1969), “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society, 37, 424-438.Grinsted, A., Moore, J.C., and Jevrejeva, S. (2004), “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process Geophysics, 11, 561-566.Gronwald, M. (2009), “Reconsidering the macroeconomics of the oil price in Germany: testing for causality in the frequency domain,” Empirical Economics, 36, 441-453.Gupta, S., Das, D., Hasim, H., and Tiwari, A.K. (2018), “The dynamic relationship between stock returns and trading volume revisited: a MODWTVAR approach,” Finance Research Letters, 27, 91-98.Hudgins, L., Friehe, C., and Mayer, M. (1993), “Wavelet transforms and atmospheric turbulence,” Physical Review Letters, 71, 3279–3282.Hui, E.C.M., and Yue S. (2006), “Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study,” Journal of Real Estate Finance and Economics, 33, 299-327.Jain, P.C., and Joh, G.-H. (1988), “The dependence between hourly prices and trading volume,” The Journal of Financial and Quantitative Analysis, 23(3), 269-283.Jumbe, C.B.L. (2004), “Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi,” Energy Economics, 26, 61-68.Kirikkaleli, D., and Güngör, H. (2021), “Comovement of commodity price indexes and energy price index: a wavelet coherence approach,” Financial Innovation, 7:15.Lee, B.-S., and Rui, O.M. (2002), “The dynamic relationship between stock returns and trading volume: domestic and cross-country evidence,” Journal of Banking & Finance, 26, 51-78.Li, X.L., Chang, T., Miller, S., Balcilar, M., and Gupta, R. (2015), “The comovement and causality between the U.S. housing and stock markets in the time and frequency domains,” International Review of Economics and Finance, 38, 220-233.Loh, L. (2013), “Co-movement of Asia-Pacific with European and US stock market returns: a cross-time-frequency analysis,” Research in International Business and Finance, 29, 1-13.Pal, D., and Mitra, S.K. (2017), “Time-frequency contained co-movement of crude oil and world food prices: a wavelet-based analysis,” Energy Economics, 62, 230-239.Pinzón, K. (2018), “Dynamics between energy consumption and economic growth in Ecuador: a granger causality analysis,” Economic Analysis and Policy, 57, 88-101.Rahman, M.M., and Kashem, M.A. (2017), “Carbon emissions, energy consumption and industrial growth in Bangladesh: empirical evidence from ARDL cointegration and Granger causality analysis,” Energy Policy, 110, 600-608.Ramsey, J.B., and Zhang, Z. (1996), “The analysis of foreign exchange data using waveform dictionaries,” Journal of Empirical Finance, 4, 341-372.Reboredo, J.C., and Rivera-Castro, M.A. (2014), “Wavelet based evidence of the impact of oil prices on stock returns,” International Review of Economics & Finance, 29, 145-176.Rua, A., and Nunes, L.C. (2009), “International comovement of stock market returns: a wavelet analysis,” Journal of Empirical Finance, 12, 632-639.Said E., and Dickey, D.A. (1984), “Testing for unit roots in autoregressive moving average models of unknown order,” Biometrika, 71, 599-607.Tiwari, A. K., M. I. Mutascu, C. T. Albulescu, and P. Kyophilavong (2015), “Frequency domain causality analysis of stock market and economic activity in India,” International Review of Economics and Finance, 39, 224-238.Toda, H. Y., and T. Yamamoto (1995), “Statistical inference in vector autoregressions with possibly integrated process,” Journal of Econometrics, 66(1-2), 225-250.Yilanci, V., Ozgur, O., and Gorus, M.S. (2021), “Stock prices and economic activity nexus in OECD countries: new evidence from an asymmetric panel Granger causality test in the frequency domain,” Financial Innovation, 7:11. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200561 en_US
