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題名 以Copula-GJR-GARCH方法探討股票與債券之連動性
An evaluation of stock and bond markets with Copula-GJR-GARCH model作者 羅永健
Luo, Yong-Jian貢獻者 張興華
Chang, Hsing-Hua
羅永健
Luo, Yong-Jian關鍵詞 相關係數結構
GJI-GARCH模型
動態copula
correlation structure
GJI-GARCH model
dynamic copula日期 2020 上傳時間 3-May-2021 10:25:04 (UTC+8) 摘要 資產報酬之間相關係數在金融市場上是非常重要的。無論是資產配置或者是風險控管都需要用到資產間的相關關係。資產間的相關關係並不是線型關係，而是非線形的。因此資產間的動態結構能更好地描述資產間的相關關係。本文以10年期美國國庫券期貨、MSCI全國家世界指數(All Country World Index, ACWI)、MSCI開發中國家指數 (Emerging Market Index, EM) 以及MSCI已開發國家指數（Developed Markets Index, DM）為例。利用附加動態條件的Copula-GJR-GARCH模型，探討債券報酬與股票指數報酬、以及股票指數報酬間的相關係數結構。最後經本文研究實證結果顯示，上述資產報酬之間的當期的相關係數顯著地受到前一期相關係數的影響。此外，近10期的報酬率所包含的資訊也會對當期相關係數有不同程度的影響。
Owing to their importance in asset allocation strategies and risk management, the comovements between the stock and bond markets have become an increasingly popular issue in financial markets. However, the correlation between assets may change over time. Therefore, the dynamic structure of assets can describe the correlation better. This paper take 10-year us Treasury futures, the MSCI All Country World Index, the MSCI Emerging Market Index and the MSCI Developed Market Index as examples. Using the Copula-GJR-GARCH model with dynamic conditions to discuss the correlation structure between asset returns. Finally, the empirical results in this paper show that the correlation of the above asset returns in the current period is significantly affected by previous period. In addition, the information contained in the return of last 10 period will also affect the correlation of the current period.參考文獻 Aloui, R., Hammoudeh, S., & Nguyen, D. K. (2013). A time-varying copula approach to oil and stock market dependence: The case of transition economies. Energy Economics, 39, 208-221.Baele, L., Bekaert, G., & Inghelbrecht, K. (2010). The determinants of stock and bond return comovements. The Review of Financial Studies, 23(6), 2374-2428.Campbell, J. Y., Sunderam, A., & Viceira, L. M. (2009). Inflation bets or deflation hedges? The changing risks of nominal bonds (0898-2937). Retrieved fromClayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65(1), 141-151.Connolly, R., Stivers, C., & Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis, 40(1), 161-194.de Goeij, P., & Marquering, W. (2004). Modeling the conditional covariance between stock and bond returns: A multivariate GARCH approach. Journal of Financial Econometrics, 2(4), 531-564.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.Guidolin, M., & Timmermann, A. (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of Applied Econometrics, 21(1), 1-22.Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55(292), 698-707.Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 705-730.Hsu, C. C., Tseng, C. P., & Wang, Y. H. (2008). Dynamic hedging with futures: A copula‐based GARCH model. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 28(11), 1095-1116.Joe, H., & Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models.Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535-551.Lai, Y., & Tseng, J.-C. (2010). The role of Chinese stock market in global stock markets: A safe haven or a hedge? International Review of Economics & Finance, 19(2), 211-218.Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54.Panchenko, V., & Wu, E. (2009). Time-varying market integration and stock and bond return concordance in emerging markets. Journal of Banking & Finance, 33(6), 1014-1021.Patton, A. J. (2006a). Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics, 21(2), 147-173.Patton, A. J. (2006b). Modelling asymmetric exchange rate dependence. International Economic Review, 47(2), 527-556.Reboredo, J. C., & Ugolini, A. (2015). Systemic risk in European sovereign debt markets: A CoVaR-copula approach. Journal of International Money and Finance, 51, 214-244.Sklar, A. (1959). Fonctions de reprtition an dimensions et leursmarges. Publications de l’Institut de Statistique de l’Universite´ de Paris, 8, 229-231.Sklar, A. (1973). Random variables, joint distribution functions, and copulas. Kybernetika, 9(6), (449)-460.Sun, W., Rachev, S., Fabozzi, F. J., & Kalev, P. S. (2009). A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence. Empirical economics, 36(1), 201.Zakamouline, V., & Koekebakker, S. (2009). Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance. Journal of Banking & Finance, 33(7), 1242-1254. 描述 碩士
國立政治大學
金融學系
107352044資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352044 資料類型 thesis dc.contributor.advisor 張興華 zh_TW dc.contributor.advisor Chang, Hsing-Hua en_US dc.contributor.author (Authors) 羅永健 zh_TW dc.contributor.author (Authors) Luo, Yong-Jian en_US dc.creator (作者) 羅永健 zh_TW dc.creator (作者) Luo, Yong-Jian en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-May-2021 10:25:04 (UTC+8) - dc.date.available 3-May-2021 10:25:04 (UTC+8) - dc.date.issued (上傳時間) 3-May-2021 10:25:04 (UTC+8) - dc.identifier (Other Identifiers) G0107352044 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/134865 - 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) 107352044 zh_TW dc.description.abstract (摘要) 資產報酬之間相關係數在金融市場上是非常重要的。無論是資產配置或者是風險控管都需要用到資產間的相關關係。資產間的相關關係並不是線型關係，而是非線形的。因此資產間的動態結構能更好地描述資產間的相關關係。本文以10年期美國國庫券期貨、MSCI全國家世界指數(All Country World Index, ACWI)、MSCI開發中國家指數 (Emerging Market Index, EM) 以及MSCI已開發國家指數（Developed Markets Index, DM）為例。利用附加動態條件的Copula-GJR-GARCH模型，探討債券報酬與股票指數報酬、以及股票指數報酬間的相關係數結構。最後經本文研究實證結果顯示，上述資產報酬之間的當期的相關係數顯著地受到前一期相關係數的影響。此外，近10期的報酬率所包含的資訊也會對當期相關係數有不同程度的影響。 zh_TW dc.description.abstract (摘要) Owing to their importance in asset allocation strategies and risk management, the comovements between the stock and bond markets have become an increasingly popular issue in financial markets. However, the correlation between assets may change over time. Therefore, the dynamic structure of assets can describe the correlation better. This paper take 10-year us Treasury futures, the MSCI All Country World Index, the MSCI Emerging Market Index and the MSCI Developed Market Index as examples. Using the Copula-GJR-GARCH model with dynamic conditions to discuss the correlation structure between asset returns. Finally, the empirical results in this paper show that the correlation of the above asset returns in the current period is significantly affected by previous period. In addition, the information contained in the return of last 10 period will also affect the correlation of the current period. en_US dc.description.tableofcontents 中文摘要 iiiAbstract iv目錄 v表目錄 vii圖目錄 viii第壹章 緒論 1第貳章 文獻回顧 3第參章 研究方法 7第一節 研究流程 7第二節 樣本與資料來源 8第三節 研究方法 12第肆章 研究結果 20第一節 資產報酬率的敘述性統計 20第二節 參數估計結果 24第伍章 研究結論 35參考文獻 37 zh_TW dc.format.extent 2284822 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352044 en_US dc.subject (關鍵詞) 相關係數結構 zh_TW dc.subject (關鍵詞) GJI-GARCH模型 zh_TW dc.subject (關鍵詞) 動態copula zh_TW dc.subject (關鍵詞) correlation structure en_US dc.subject (關鍵詞) GJI-GARCH model en_US dc.subject (關鍵詞) dynamic copula en_US dc.title (題名) 以Copula-GJR-GARCH方法探討股票與債券之連動性 zh_TW dc.title (題名) An evaluation of stock and bond markets with Copula-GJR-GARCH model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aloui, R., Hammoudeh, S., & Nguyen, D. K. (2013). A time-varying copula approach to oil and stock market dependence: The case of transition economies. Energy Economics, 39, 208-221.Baele, L., Bekaert, G., & Inghelbrecht, K. (2010). The determinants of stock and bond return comovements. The Review of Financial Studies, 23(6), 2374-2428.Campbell, J. Y., Sunderam, A., & Viceira, L. M. (2009). Inflation bets or deflation hedges? The changing risks of nominal bonds (0898-2937). Retrieved fromClayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65(1), 141-151.Connolly, R., Stivers, C., & Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis, 40(1), 161-194.de Goeij, P., & Marquering, W. (2004). Modeling the conditional covariance between stock and bond returns: A multivariate GARCH approach. Journal of Financial Econometrics, 2(4), 531-564.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.Guidolin, M., & Timmermann, A. (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of Applied Econometrics, 21(1), 1-22.Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55(292), 698-707.Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 705-730.Hsu, C. C., Tseng, C. P., & Wang, Y. H. (2008). Dynamic hedging with futures: A copula‐based GARCH model. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 28(11), 1095-1116.Joe, H., & Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models.Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535-551.Lai, Y., & Tseng, J.-C. (2010). The role of Chinese stock market in global stock markets: A safe haven or a hedge? International Review of Economics & Finance, 19(2), 211-218.Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54.Panchenko, V., & Wu, E. (2009). Time-varying market integration and stock and bond return concordance in emerging markets. Journal of Banking & Finance, 33(6), 1014-1021.Patton, A. J. (2006a). Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics, 21(2), 147-173.Patton, A. J. (2006b). Modelling asymmetric exchange rate dependence. International Economic Review, 47(2), 527-556.Reboredo, J. C., & Ugolini, A. (2015). Systemic risk in European sovereign debt markets: A CoVaR-copula approach. Journal of International Money and Finance, 51, 214-244.Sklar, A. (1959). Fonctions de reprtition an dimensions et leursmarges. Publications de l’Institut de Statistique de l’Universite´ de Paris, 8, 229-231.Sklar, A. (1973). Random variables, joint distribution functions, and copulas. Kybernetika, 9(6), (449)-460.Sun, W., Rachev, S., Fabozzi, F. J., & Kalev, P. S. (2009). A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence. Empirical economics, 36(1), 201.Zakamouline, V., & Koekebakker, S. (2009). Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance. Journal of Banking & Finance, 33(7), 1242-1254. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100419 en_US
