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題名 基於產業類別之S&P500與美國十年期公債DCC動態條件相關性分析
The analysis of Dynamic Conditional Correlations between S&P500 and US 10 Year Treasury based on different industries
作者 何建志
He, Chien-Chih
貢獻者 張興華
Chang, Hsing-Hua
何建志
He,Chien-Chih
關鍵詞 股債動態條件相關性
產業別
資料頻率
DCC MV-GARCH Model
日期 2023
上傳時間 1-Sep-2023 14:47:20 (UTC+8)
摘要 股票與債券為市場上最為普遍的金融資產，深入了解股債相關性可以增進資產配置效益、提升股債再平衡效率，活化避險策略。而美國又是全球發展最蓬勃的金融市場，因此本研究以美國十年期公債與S & P 500及S & P 500不同產業為標的，評估其股債動態條件相關係數。
本研究利用DCC MV-GARCH模型計算出之股債動態條件相關係數，而第一個研究目標，即為了解股債動態條件相關係數在日資料與月資料中，隨時間的走勢變化。實證顯示，股債動態條件相關係數在2022年聯準會快速升息下，有明顯的上升趨勢，且日資料較月資料存在更明顯的波動持續性與叢聚性。
第二與第三個研究目標，分別為利用OLS迴歸分析評估VIX、MOVE、美元指數、黃金現貨價格、產業股價指數交易量對股債動態條件相關係數於不同產業與不同資料頻率之影響。實證結果顯示，面對VIX指數改變，股債動態條件相關係數在不同資料頻率下會有相反之變化，可能原因包含投資人情緒、再平衡、資金調配、風險控制、安全性資產與風險性資產間的轉移等行為。而面對MOVE指數、美元指數變動，股債動態條件相關係數同樣在不同資料頻率下容易有相反之變化，可能原因包含安全性資產與風險性資產間的轉移、總體經濟、貨幣政策等因素。產業之特性、資料頻率的差異都會造成股債動態條件相關係數在變化幅度上的異同。股價指數交易量變動，則容易在短期內造成股債動態條件相關係數正向變化。
Stocks and bonds are the most common financial assets in the market. Understanding the correlation between them can enhance asset allocation efficiency, improve stock-bond rebalancing, and activate hedging strategies. This study focuses on the dynamic conditional correlation between U.S. ten-year Treasury bonds, the S&P 500 index, and its various sectors.

Using the DCC MV-GARCH model, we calculate the dynamic conditional correlation. The first goal is to track how this correlation changes over time in daily and monthly data. Empirical evidence shows that amid the 2022 Federal Reserve interest rate hikes, the correlation exhibited a notable upward trend. Daily data also displayed more pronounced volatility and clustering compared to monthly data.

The second and third objectives involve analyzing the impact of VIX, MOVE, the U.S. dollar index, gold prices, and industry stock index trading volumes on the dynamic conditional correlation in different industries and data frequencies. Results indicate that changes in the VIX index lead to opposite effects at different data frequencies, possibly due to investor sentiment, rebalancing, capital allocation, risk control, and asset transfers between safety and risk categories. Similar effects are observed with changes in the MOVE index and the U.S. dollar index. Differences in industry characteristics and data frequency contribute to variations in the dynamic conditional correlation. Changes in stock index trading volumes can induce short-term positive dynamic conditional correlations.
參考文獻 1. 李昀 (2021)。聯準會量化寬鬆政策對美國股債動態條件相關性之影響。國立政治大學財務管理研究所。
2. 張維敉（2002）。金融危機與風險外溢─DCC模型之應用。國立中央大學財務金融研究所。
3. 陳旭昇（2013）。時間序列分析--總體經濟與財務金融之應用。台灣：東華。
4. Baker, M. P., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680.
5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3) (1986), pp. 307-327
6. Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics 69:542–47.
7. Bollerslev, T., R. Engle, and J.M. Wooldridge. (1988). A Capital Asset Pricing Model with Time Varying Covariance. Journal of Political Economy, 96, 116-131.
8. Cao, N., Galvani, V., & Gubellini, S. (2017). Firm-specific stock and bond predictability: New evidence from Canada. International Review of Economics & Finance, 51, 174–192.
9. Christie, A. A. (1982), The stochastic behavior of common stock variances: Value Leverage and interest rate effects. Journal of Financial Economics, 10, 407-432.
10. Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007.
11. Engle, R. F. (2002) Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20, 339-350.
12. Gokmenoglu, K. K., and Hadood, A. A. A. (2020). Impact of US unconventional monetary policy on dynamic stock-bond correlations: portfolio rebalancing and signalling channel effects. Finance Research Letters, forthcoming.
13. Gomes, P., & Taamouti, A. (2016). In search of the determinants of European asset market comovements. International Review of Economics & Finance, 44, 103–117.
14. Hassani, H., & Yeganegi, M. R. (2020). Selecting optimal lag order in Ljung-Box test. Physica A: Statistical Mechanics and its Applications, 541, Article 123700.
15. L. Fang, H. Yu, Y. Huang. (2018) The role of investor sentiment in the long-term correlation between U.S. stock and bond markets. International Review of Economics and Finance, 58, pp. 127-139
16. Kalotychou, E., Staikouras, S. K., & Zhao, G. (2014). The role of correlation dynamics in sector allocation. Journal of Banking and Finance, 48, 1–12.
17. M.H. Kim, L. Sun. (2017). Dynamic conditional correlations between Chinese sector returns and the S&P 500 index: An interpretation based on investment shocks. International Review of Economics & Finance, 48, pp. 309-325.
18. Kozak, S. (2022). Dynamics of stock and bond returns. Journal of Monetary Economics, 126, 188– 209.
19. Ljung, G. , Box, G. (1978). On a measure of lack of fit in time series models. Biometrika 65, 297-303.
20. Lin, Kaitao. , Gurrola-Perez, Pedro. , Speth, Bill. (2022). Circuit Breakers and Market Quality. WFE Research Working Paper no. 3
21. Markowitz, Harry M. (1952). Portfolio Selection. Journal of Finance, pp. 77-91.
22. Nelson, C.R. , Plosser, C.I. (1982) Trends and random walks in macroeconomic time series: some evidence and implications. J. Monetary Econ., 10 (2), pp. 139-l62
23. Nelson, D.B. (1989), Modeling stock market volatility changes. Proceedings of the American Statistical Association, Business and Economic Statistics Section, 93-98.
24. Nelson D.B. (1990). ARCH models as diffusion approximations. Journal of Econometrics, 45 (1), pp. 7-38.
25. Papanikolaou, D. (2011). Investment shocks and asset prices. Journal of Political Economy, 119, 639–685.
26. Phylaktis, K., & Xia, L. (2009). Equity market comovement and contagion: A sectoral perspective. Financial Management, 38(2), 381–409.
27. R.S.Tsay. (2010) Analysis of Financial Time Series, John Wiley & Sons, New York.
28. Rattray, S. , N. Granger, C. R. Harvey, and O. Van Hemert (2020). Strategic rebalancing. Journal of Portfolio Management, forthcoming.
29. R.H.Shumway,D.S.Stoffer (2011). Time Series Analysis and Its Applications with R Examples,Springer. NewYork.
30. R.J.Hyndman,G.Athanasopoulos (2018). Forecasting : Principles and Practice. Otext.
31. Tobin, J. (1958). Liquidity Preference as Behavior Towards Risk. Review of Economic Studies, 25, 65-86. [960]
描述 碩士
國立政治大學
金融學系
110352005
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110352005
資料類型 thesis
dc.contributor.advisor 張興華zh_TW
dc.contributor.advisor Chang, Hsing-Huaen_US
dc.contributor.author (Authors) 何建志zh_TW
dc.contributor.author (Authors) He,Chien-Chihen_US
dc.creator (作者) 何建志zh_TW
dc.creator (作者) He, Chien-Chihen_US
dc.date (日期) 2023en_US
dc.date.accessioned 1-Sep-2023 14:47:20 (UTC+8)-
dc.date.available 1-Sep-2023 14:47:20 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2023 14:47:20 (UTC+8)-
dc.identifier (Other Identifiers) G0110352005en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146859-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 110352005zh_TW
dc.description.abstract (摘要) 股票與債券為市場上最為普遍的金融資產，深入了解股債相關性可以增進資產配置效益、提升股債再平衡效率，活化避險策略。而美國又是全球發展最蓬勃的金融市場，因此本研究以美國十年期公債與S & P 500及S & P 500不同產業為標的，評估其股債動態條件相關係數。
本研究利用DCC MV-GARCH模型計算出之股債動態條件相關係數，而第一個研究目標，即為了解股債動態條件相關係數在日資料與月資料中，隨時間的走勢變化。實證顯示，股債動態條件相關係數在2022年聯準會快速升息下，有明顯的上升趨勢，且日資料較月資料存在更明顯的波動持續性與叢聚性。
第二與第三個研究目標，分別為利用OLS迴歸分析評估VIX、MOVE、美元指數、黃金現貨價格、產業股價指數交易量對股債動態條件相關係數於不同產業與不同資料頻率之影響。實證結果顯示，面對VIX指數改變，股債動態條件相關係數在不同資料頻率下會有相反之變化，可能原因包含投資人情緒、再平衡、資金調配、風險控制、安全性資產與風險性資產間的轉移等行為。而面對MOVE指數、美元指數變動，股債動態條件相關係數同樣在不同資料頻率下容易有相反之變化，可能原因包含安全性資產與風險性資產間的轉移、總體經濟、貨幣政策等因素。產業之特性、資料頻率的差異都會造成股債動態條件相關係數在變化幅度上的異同。股價指數交易量變動，則容易在短期內造成股債動態條件相關係數正向變化。		zh_TW
dc.description.abstract (摘要) Stocks and bonds are the most common financial assets in the market. Understanding the correlation between them can enhance asset allocation efficiency, improve stock-bond rebalancing, and activate hedging strategies. This study focuses on the dynamic conditional correlation between U.S. ten-year Treasury bonds, the S&P 500 index, and its various sectors.

Using the DCC MV-GARCH model, we calculate the dynamic conditional correlation. The first goal is to track how this correlation changes over time in daily and monthly data. Empirical evidence shows that amid the 2022 Federal Reserve interest rate hikes, the correlation exhibited a notable upward trend. Daily data also displayed more pronounced volatility and clustering compared to monthly data.

The second and third objectives involve analyzing the impact of VIX, MOVE, the U.S. dollar index, gold prices, and industry stock index trading volumes on the dynamic conditional correlation in different industries and data frequencies. Results indicate that changes in the VIX index lead to opposite effects at different data frequencies, possibly due to investor sentiment, rebalancing, capital allocation, risk control, and asset transfers between safety and risk categories. Similar effects are observed with changes in the MOVE index and the U.S. dollar index. Differences in industry characteristics and data frequency contribute to variations in the dynamic conditional correlation. Changes in stock index trading volumes can induce short-term positive dynamic conditional correlations.		en_US
dc.description.tableofcontents 第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 6
第三節 研究架構與流程 8
第貳章 文獻回顧 9
第一節 財務時間序列之相關波動性研究模型 9
第二節 DCC MV-GARCH模型之相關應用 10
第三節 股債相關性之文獻探討 11
第參章 研究方法 13
第一節 最適落後期數選擇 13
第二節 時間序列資料單根檢定 13
第三節 檢定ARCH效果 15
第四節 DCC MV-GARCH模型之設定 16
第五節 迴歸分析模型設定 19
第肆章 資料處理 21
第一節 應變數資料處理 21
第二節 解釋變數資料處理 25
第伍章 實證研究結果 28
第一節 資料初步分析 28
第二節 時間序列資料單根檢定 34
第三節 DCC MV-GARCH模型設定 35
第四節 迴歸分析結果 47
第陸章 結論與未來研究方向 56
第一節 結論 56
第二節 未來研究方向 57
參考文獻 58		zh_TW
dc.format.extent 2822687 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110352005en_US
dc.subject (關鍵詞) 股債動態條件相關性zh_TW
dc.subject (關鍵詞) 產業別zh_TW
dc.subject (關鍵詞) 資料頻率zh_TW
dc.subject (關鍵詞) DCC MV-GARCH Modelen_US
dc.title (題名) 基於產業類別之S&P500與美國十年期公債DCC動態條件相關性分析zh_TW
dc.title (題名) The analysis of Dynamic Conditional Correlations between S&P500 and US 10 Year Treasury based on different industriesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. 李昀 (2021)。聯準會量化寬鬆政策對美國股債動態條件相關性之影響。國立政治大學財務管理研究所。
2. 張維敉（2002）。金融危機與風險外溢─DCC模型之應用。國立中央大學財務金融研究所。
3. 陳旭昇（2013）。時間序列分析--總體經濟與財務金融之應用。台灣：東華。
4. Baker, M. P., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680.
5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3) (1986), pp. 307-327
6. Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics 69:542–47.
7. Bollerslev, T., R. Engle, and J.M. Wooldridge. (1988). A Capital Asset Pricing Model with Time Varying Covariance. Journal of Political Economy, 96, 116-131.
8. Cao, N., Galvani, V., & Gubellini, S. (2017). Firm-specific stock and bond predictability: New evidence from Canada. International Review of Economics & Finance, 51, 174–192.
9. Christie, A. A. (1982), The stochastic behavior of common stock variances: Value Leverage and interest rate effects. Journal of Financial Economics, 10, 407-432.
10. Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007.
11. Engle, R. F. (2002) Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20, 339-350.
12. Gokmenoglu, K. K., and Hadood, A. A. A. (2020). Impact of US unconventional monetary policy on dynamic stock-bond correlations: portfolio rebalancing and signalling channel effects. Finance Research Letters, forthcoming.
13. Gomes, P., & Taamouti, A. (2016). In search of the determinants of European asset market comovements. International Review of Economics & Finance, 44, 103–117.
14. Hassani, H., & Yeganegi, M. R. (2020). Selecting optimal lag order in Ljung-Box test. Physica A: Statistical Mechanics and its Applications, 541, Article 123700.
15. L. Fang, H. Yu, Y. Huang. (2018) The role of investor sentiment in the long-term correlation between U.S. stock and bond markets. International Review of Economics and Finance, 58, pp. 127-139
16. Kalotychou, E., Staikouras, S. K., & Zhao, G. (2014). The role of correlation dynamics in sector allocation. Journal of Banking and Finance, 48, 1–12.
17. M.H. Kim, L. Sun. (2017). Dynamic conditional correlations between Chinese sector returns and the S&P 500 index: An interpretation based on investment shocks. International Review of Economics & Finance, 48, pp. 309-325.
18. Kozak, S. (2022). Dynamics of stock and bond returns. Journal of Monetary Economics, 126, 188– 209.
19. Ljung, G. , Box, G. (1978). On a measure of lack of fit in time series models. Biometrika 65, 297-303.
20. Lin, Kaitao. , Gurrola-Perez, Pedro. , Speth, Bill. (2022). Circuit Breakers and Market Quality. WFE Research Working Paper no. 3
21. Markowitz, Harry M. (1952). Portfolio Selection. Journal of Finance, pp. 77-91.
22. Nelson, C.R. , Plosser, C.I. (1982) Trends and random walks in macroeconomic time series: some evidence and implications. J. Monetary Econ., 10 (2), pp. 139-l62
23. Nelson, D.B. (1989), Modeling stock market volatility changes. Proceedings of the American Statistical Association, Business and Economic Statistics Section, 93-98.
24. Nelson D.B. (1990). ARCH models as diffusion approximations. Journal of Econometrics, 45 (1), pp. 7-38.
25. Papanikolaou, D. (2011). Investment shocks and asset prices. Journal of Political Economy, 119, 639–685.
26. Phylaktis, K., & Xia, L. (2009). Equity market comovement and contagion: A sectoral perspective. Financial Management, 38(2), 381–409.
27. R.S.Tsay. (2010) Analysis of Financial Time Series, John Wiley & Sons, New York.
28. Rattray, S. , N. Granger, C. R. Harvey, and O. Van Hemert (2020). Strategic rebalancing. Journal of Portfolio Management, forthcoming.
29. R.H.Shumway,D.S.Stoffer (2011). Time Series Analysis and Its Applications with R Examples,Springer. NewYork.
30. R.J.Hyndman,G.Athanasopoulos (2018). Forecasting : Principles and Practice. Otext.
31. Tobin, J. (1958). Liquidity Preference as Behavior Towards Risk. Review of Economic Studies, 25, 65-86. [960]		zh_TW