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題名 解構美國股市報酬與利差在瞬時相位上的因果關係
An Analysis of The Causality Between The US Stock Returns and Spreads on Instantaneous Phase
作者 陳科全
Chen, Ke-Chuan
貢獻者 徐士勛
Shiu, Shr-Shiun
陳科全
Chen, Ke-Chuan
關鍵詞 股市報酬
公債利差
因果拆解法
經驗模態分解法
Granger 因果關係檢定法
日期 2019
上傳時間 1-Jul-2019 11:03:29 (UTC+8)
摘要 由於美國的公債市場與股票市場對於全球經濟體系具相當程度的影響性，且公債市場裡不同種類的利差皆可用以預測股市的報酬，兩者應間存在一定的關聯性，故本文想藉由客觀的統計方法探討美國的利差與股市報酬間是否存在因果關係。
根據美國的公債長短天期利差與S\\&P 500日報酬率的歷史資料發現，殖利率曲線出現或接近反轉(利差為負)後，S\\&P 500 日報酬率在短期內會因市場恐慌而出現較大的波動，但S\\&P 500指數在之後大多仍維持1年半到2年的多頭漲勢，漲勢結束後才隨即出現衰退，故我們認為股市報酬的波動可能是由公債長短天期利差所導致，兩者間可能存在因果關係。而由TED 利差與LOIS利差和S\\&P 500日報酬率的歷史資料皆發現雖然這兩個利差在金融危機過後皆不超過100個基點，但在這兩個利差出現比平常較大的值或出現持續上升走勢時，S\\&P 500日報酬率在該期間附近也出現較大波動，故我們猜測其之間亦可能存在因果關係。
本文藉由傳統的Granger 因果關係檢定法以及因果拆解法進行因果關係的認定；其中，因果拆解法不只適用於分析各種型態的資料系統，也能避免忽略具同時性與相互性的因果關係，最後還可得出兩筆資料在不同時間尺度下的因果關係。Granger 因果關係檢定法以及因果拆解法的實證結果皆證明不同利差與股市報酬間確實存在因果關係，前者的實證結果為不同利差與股市報酬間存在雙向因果關係，彼此會互相影響；後者的結果則發現了不同利差皆會影響股市報酬的短期波動，且不同利差的中期波動會受到股市報酬的影響；最後利差與股市報酬在長期時具相互因果關係。大致而言，我們透過因果拆解法的結果也符合我們在歷史資料上觀察到的一些不同利差與股市報酬間的現象。
參考文獻 Ait-Sahalia, Y., Andritzky, J., Jobst, A., Nowak, S., & Tamirisa, N. (2012). Market response to policy initiatives during the global financial crisis. Journal of International Economics, 87(1), 162–177.
Atje, R., & Jovanovic, B. (1993). Stock markets and development. European Economic Review, 37(2-3), 632–640.
Baek, E. G., & Brock, W. A. (1992). A nonparametric test for independence of a multivariate time series. Statistica Sinica, 2(1), 137–156.
Bauer, M. D., & Mertens, T. M. (2018). Economic forecasts with the yield curve. FRBSF Economic Letter, 7.
Bruche, M., & Suarez, J. (2010). Deposit insurance and money market freezes. Journal of Monetary Economics, 57(1), 45–61.
Campbell, J. Y., & Mankiw, N. G. (1989). Consumption, income, and interest rates: Reinterpreting the time series evidence. NBER Macroeconomics Annual, 4, 185–216.
Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383–403.
Comincioli, B. (1996). The stock market as a leading indicator: An application of Granger causality. University Avenue Undergraduate Journal of Economics, 1(1).
Davidson, J. E. H., Hendry, D. F., Srba, F., & Yeo, S. (1978). Econometric modelling of the aggregate time-series relationship between consumers’ expenditure and income in the United Kingdom. The Economic Journal, 88(352), 661–692.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of The Econometric Society, 55(2), 251–276.
Estrella, A., & Hardouvelis, G. A. (1991). The term structure as a predictor of real economic activity. The Journal of Finance, 46(2), 555–576.
Evgenidis, A., Papadamou, S., & Siriopoulos, C. (2018). The yield spread’s ability to forecast economic activity: What have we learned after 30 years of studies? Journal of Business Research.
Evgenidis, A., & Siriopoulos, C. (2016). An explanation of spread’s ability to predict economic activity: A regime switching model. Journal of Economic Studies, 43(3), 488–503.
Fama, E. F. (1990). Stock returns, expected returns, and real activity. The journal of Finance, 45(4), 1089–1108.
Fernandez-Perez, A., Fernández-Rodríguez, F., & Sosvilla-Rivero, S. (2014). The term structure of interest rates as predictor of stock returns: evidence for the IBEX 35 during a bear market. International Review of Economics & Finance, 31, 21–33.
Frank, N., & Hesse, H. (2009). Financial spillovers to emerging markets during the global financial crisis (No. 9-104). International Monetary Fund.
Freixas, X., Martin, A., & Skeie, D. (2011). Bank liquidity, interbank markets, and monetary policy. The Review of Financial Studies, 24(8), 2656–2692.
Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of The American Statistical Association, 77(378), 304–313.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of The Econometric Society, 37(3), 424–438.
Gupta, R., Risse, M., Volkman, D. A., & Wohar, M. E. (2019). The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data. The North American Journal of Economics and Finance, 47, 391–405.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305–333.
Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639–1664.
Hjalmarsson, E. (2010). Predicting global stock returns. Journal of Financial and Quantitative Analysis, 45(1), 49–80.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.
In, F., Cui, J., & Maharaj, E. A. (2012). The impact of a new term auction facility on Libor–OIS spreads and volatility transmission between money and mortgage markets during the subprime crisis. Journal of International Money and Finance, 31(5), 1106–1125.
Jiang, L., & Bai, L. (2017). Revisiting the Granger causality relationship between energy consumption and economic growth in China: A multi-timescale decomposition approach. Sustainability, 9(12), 2299.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231–254.
Kawaller, I. G. (1997). The TED spread. Derivatives Quarterly, 3(3), 46–54.
Lashgari, M. (2000). The role of TED spread and confidence index in explaining the behavior of stock prices. American Business Review, 18(2), 9.
Laurent, R. D. (1988). An interest rate-based indicator of monetary policy. Economic Perspectives, 12(1), 3–14.
Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. American Economic Review, 88(3), 537–558.
Liu, W., Resnick, B. G., & Shoesmith, G. L. (2004). Market timing of international stock markets using the yield spread. Journal of Financial Research, 27(3), 373–391.
Nava, N., Di Matteo, T., & Aste, T. (2018). Dynamic correlations at different timescales with empirical mode decomposition. Physica A: Statistical Mechanics and Its Applications, 502, 534–544.
Pearce, D. K., & Roley, V. V. (1983). The reaction of stock prices to unanticipated changes in money: A note. The Journal of Finance, 38(4), 1323–1333.
Rapach, D. E., Wohar, M. E., & Rangvid, J. (2005). Macro variables and international stock return predictability. International Journal of Forecasting, 21(1), 137–166.
Resnick, B. G., & Shoesmith, G. L. (2002). Using the yield curve to time the stock market. Financial Analysts Journal, 58(3), 82–90.
Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.
Toda, H. Y., & Phillips, P. C. B. (1994). Vector autoregression and causality: A theoretical overview and simulation study. Econometric Reviews, 13(2), 259–285.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225–250.
Tse, Y., & Booth, G. G. (1996). Common volatility and volatility spillovers between US and Eurodollar interest rates: Evidence from the futures market. Journal of Economics and Business, 48(3), 299–312.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1–41.
Yang, A. C., Peng, C.-K., & Huang, N. E. (2018). Causal decomposition in the mutual causation system. Nature Communications, 9(1), 3378.
描述 碩士
國立政治大學
經濟學系
106258037
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1062580372
資料類型 thesis
dc.contributor.advisor 徐士勛zh_TW
dc.contributor.advisor Shiu, Shr-Shiunen_US
dc.contributor.author (Authors) 陳科全zh_TW
dc.contributor.author (Authors) Chen, Ke-Chuanen_US
dc.creator (作者) 陳科全zh_TW
dc.creator (作者) Chen, Ke-Chuanen_US
dc.date (日期) 2019en_US
dc.date.accessioned 1-Jul-2019 11:03:29 (UTC+8)-
dc.date.available 1-Jul-2019 11:03:29 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2019 11:03:29 (UTC+8)-
dc.identifier (Other Identifiers) G1062580372en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124215-
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) 106258037zh_TW
dc.description.abstract (摘要) 由於美國的公債市場與股票市場對於全球經濟體系具相當程度的影響性，且公債市場裡不同種類的利差皆可用以預測股市的報酬，兩者應間存在一定的關聯性，故本文想藉由客觀的統計方法探討美國的利差與股市報酬間是否存在因果關係。
根據美國的公債長短天期利差與S\\&P 500日報酬率的歷史資料發現，殖利率曲線出現或接近反轉(利差為負)後，S\\&P 500 日報酬率在短期內會因市場恐慌而出現較大的波動，但S\\&P 500指數在之後大多仍維持1年半到2年的多頭漲勢，漲勢結束後才隨即出現衰退，故我們認為股市報酬的波動可能是由公債長短天期利差所導致，兩者間可能存在因果關係。而由TED 利差與LOIS利差和S\\&P 500日報酬率的歷史資料皆發現雖然這兩個利差在金融危機過後皆不超過100個基點，但在這兩個利差出現比平常較大的值或出現持續上升走勢時，S\\&P 500日報酬率在該期間附近也出現較大波動，故我們猜測其之間亦可能存在因果關係。
本文藉由傳統的Granger 因果關係檢定法以及因果拆解法進行因果關係的認定；其中，因果拆解法不只適用於分析各種型態的資料系統，也能避免忽略具同時性與相互性的因果關係，最後還可得出兩筆資料在不同時間尺度下的因果關係。Granger 因果關係檢定法以及因果拆解法的實證結果皆證明不同利差與股市報酬間確實存在因果關係，前者的實證結果為不同利差與股市報酬間存在雙向因果關係，彼此會互相影響；後者的結果則發現了不同利差皆會影響股市報酬的短期波動，且不同利差的中期波動會受到股市報酬的影響；最後利差與股市報酬在長期時具相互因果關係。大致而言，我們透過因果拆解法的結果也符合我們在歷史資料上觀察到的一些不同利差與股市報酬間的現象。		zh_TW
dc.description.tableofcontents 1 緒論 5
1.1 研究動機與目的 5
1.2 研究架構 8
2 文獻回顧 9
2.1 因果關係(causality) 9
2.1.1 Granger 因果關係(Granger causality) 10
2.1.2 因果拆解法(causal decomposition) 11
2.2 不同利差與股市間的關係 12
2.2.1 公債長短天期利差與股市間的關係 13
2.2.2 TED 利差、LOIS 利差與股市間的關係 16
3 研究方法 18
3.1 瞬時相位上的因果關係(causality on instantaneous phase) 18
3.1.1 希爾伯特黃轉換(Hilbert-Huang transform, HHT) 18
3.1.2 相對因果關係(relative causality) 22
3.2 Granger 因果關係檢定(Granger causality test) 24
4 研究資料26
4.1 10 年、2 年期公債利差與S&P 500 日報酬率 27
4.2 10 年、3 個月期公債利差與S&P 500 日報酬率 28
4.3 TED 利差與S&P 500 日報酬率 30
4.4 LOIS 利差與S&P 500 日報酬率 31
5 實證結果 32
5.1 因果拆解法的實證結果 32
5.1.1 股市報酬與公債長短天期利差 35
5.1.2 股市報酬與TED 利差和LOIS 利差 38
5.2 Granger 因果關係的實證結果 41
6 結論與建議 44
附錄 46
參考文獻 50		zh_TW
dc.format.extent 3293864 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1062580372en_US
dc.subject (關鍵詞) 股市報酬zh_TW
dc.subject (關鍵詞) 公債利差zh_TW
dc.subject (關鍵詞) 因果拆解法zh_TW
dc.subject (關鍵詞) 經驗模態分解法zh_TW
dc.subject (關鍵詞) Granger 因果關係檢定法zh_TW
dc.title (題名) 解構美國股市報酬與利差在瞬時相位上的因果關係zh_TW
dc.title (題名) An Analysis of The Causality Between The US Stock Returns and Spreads on Instantaneous Phaseen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ait-Sahalia, Y., Andritzky, J., Jobst, A., Nowak, S., & Tamirisa, N. (2012). Market response to policy initiatives during the global financial crisis. Journal of International Economics, 87(1), 162–177.
Atje, R., & Jovanovic, B. (1993). Stock markets and development. European Economic Review, 37(2-3), 632–640.
Baek, E. G., & Brock, W. A. (1992). A nonparametric test for independence of a multivariate time series. Statistica Sinica, 2(1), 137–156.
Bauer, M. D., & Mertens, T. M. (2018). Economic forecasts with the yield curve. FRBSF Economic Letter, 7.
Bruche, M., & Suarez, J. (2010). Deposit insurance and money market freezes. Journal of Monetary Economics, 57(1), 45–61.
Campbell, J. Y., & Mankiw, N. G. (1989). Consumption, income, and interest rates: Reinterpreting the time series evidence. NBER Macroeconomics Annual, 4, 185–216.
Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383–403.
Comincioli, B. (1996). The stock market as a leading indicator: An application of Granger causality. University Avenue Undergraduate Journal of Economics, 1(1).
Davidson, J. E. H., Hendry, D. F., Srba, F., & Yeo, S. (1978). Econometric modelling of the aggregate time-series relationship between consumers’ expenditure and income in the United Kingdom. The Economic Journal, 88(352), 661–692.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of The Econometric Society, 55(2), 251–276.
Estrella, A., & Hardouvelis, G. A. (1991). The term structure as a predictor of real economic activity. The Journal of Finance, 46(2), 555–576.
Evgenidis, A., Papadamou, S., & Siriopoulos, C. (2018). The yield spread’s ability to forecast economic activity: What have we learned after 30 years of studies? Journal of Business Research.
Evgenidis, A., & Siriopoulos, C. (2016). An explanation of spread’s ability to predict economic activity: A regime switching model. Journal of Economic Studies, 43(3), 488–503.
Fama, E. F. (1990). Stock returns, expected returns, and real activity. The journal of Finance, 45(4), 1089–1108.
Fernandez-Perez, A., Fernández-Rodríguez, F., & Sosvilla-Rivero, S. (2014). The term structure of interest rates as predictor of stock returns: evidence for the IBEX 35 during a bear market. International Review of Economics & Finance, 31, 21–33.
Frank, N., & Hesse, H. (2009). Financial spillovers to emerging markets during the global financial crisis (No. 9-104). International Monetary Fund.
Freixas, X., Martin, A., & Skeie, D. (2011). Bank liquidity, interbank markets, and monetary policy. The Review of Financial Studies, 24(8), 2656–2692.
Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of The American Statistical Association, 77(378), 304–313.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of The Econometric Society, 37(3), 424–438.
Gupta, R., Risse, M., Volkman, D. A., & Wohar, M. E. (2019). The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data. The North American Journal of Economics and Finance, 47, 391–405.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305–333.
Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639–1664.
Hjalmarsson, E. (2010). Predicting global stock returns. Journal of Financial and Quantitative Analysis, 45(1), 49–80.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.
In, F., Cui, J., & Maharaj, E. A. (2012). The impact of a new term auction facility on Libor–OIS spreads and volatility transmission between money and mortgage markets during the subprime crisis. Journal of International Money and Finance, 31(5), 1106–1125.
Jiang, L., & Bai, L. (2017). Revisiting the Granger causality relationship between energy consumption and economic growth in China: A multi-timescale decomposition approach. Sustainability, 9(12), 2299.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231–254.
Kawaller, I. G. (1997). The TED spread. Derivatives Quarterly, 3(3), 46–54.
Lashgari, M. (2000). The role of TED spread and confidence index in explaining the behavior of stock prices. American Business Review, 18(2), 9.
Laurent, R. D. (1988). An interest rate-based indicator of monetary policy. Economic Perspectives, 12(1), 3–14.
Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. American Economic Review, 88(3), 537–558.
Liu, W., Resnick, B. G., & Shoesmith, G. L. (2004). Market timing of international stock markets using the yield spread. Journal of Financial Research, 27(3), 373–391.
Nava, N., Di Matteo, T., & Aste, T. (2018). Dynamic correlations at different timescales with empirical mode decomposition. Physica A: Statistical Mechanics and Its Applications, 502, 534–544.
Pearce, D. K., & Roley, V. V. (1983). The reaction of stock prices to unanticipated changes in money: A note. The Journal of Finance, 38(4), 1323–1333.
Rapach, D. E., Wohar, M. E., & Rangvid, J. (2005). Macro variables and international stock return predictability. International Journal of Forecasting, 21(1), 137–166.
Resnick, B. G., & Shoesmith, G. L. (2002). Using the yield curve to time the stock market. Financial Analysts Journal, 58(3), 82–90.
Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.
Toda, H. Y., & Phillips, P. C. B. (1994). Vector autoregression and causality: A theoretical overview and simulation study. Econometric Reviews, 13(2), 259–285.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225–250.
Tse, Y., & Booth, G. G. (1996). Common volatility and volatility spillovers between US and Eurodollar interest rates: Evidence from the futures market. Journal of Economics and Business, 48(3), 299–312.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1–41.
Yang, A. C., Peng, C.-K., & Huang, N. E. (2018). Causal decomposition in the mutual causation system. Nature Communications, 9(1), 3378.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU201900048en_US