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| Title: | 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 |
| Authors: | 羅永健
Luo, Yong-Jian |
| Contributors: | 張興華
Chang, Hsing-Hua
羅永健
Luo, Yong-Jian |
| Keywords: | 相關係數結構
GJI-GARCH模型
動態copula
correlation structure
GJI-GARCH model
dynamic copula |
| Date: | 2020 |
| Issue Date: | 2021-05-03 10:25:04 (UTC+8) |
| Abstract: | 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. |
| Reference: | 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 from
Clayton, 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. |
| Description: | 碩士
國立政治大學
金融學系
107352044 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107352044 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100419 |
| Appears in Collections: | [Department of Money and Banking] Theses
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