TY - GEN
T1 - Comparison of BEKK GARCH and DCC GARCH models
T2 - 6th International Conference on Advanced Data Mining and Applications, ADMA 2010
AU - Huang, Yiyu
AU - Su, Wenjing
AU - Li, Xiang
PY - 2010
Y1 - 2010
N2 - Modeling volatility and co-volatility of a few zero-coupon bonds is a fundamental element in the field of fix-income risk evaluation. Multivariate GARCH model (MGARCH), an extension of the well-known univariate GARCH, is one of the most useful tools in modeling the co-movement of multivariate time series with time-varying covariance matrix. Grounded on the review of various formulations of multivariate GARCH model, this paper estimates two MGARCH models, BEKK and DCC form, respectively, based on the data of three AAA-rated Euro zero-coupon bonds with different maturities (6 months/1 year/2 years). Post-model diagnostics indicates satisfying fitting performance of these estimated MGARCH models. Moreover, this paper provides comparison on the goodness of fit and forecasting performances of these forms by adopting the mean absolute error (MAE) criterion. Throughout this application, the conclusion can be drawn that significant fitting and forecasting performances originate from the trade-off between parsimony and flexibility of the MGARCH models.
AB - Modeling volatility and co-volatility of a few zero-coupon bonds is a fundamental element in the field of fix-income risk evaluation. Multivariate GARCH model (MGARCH), an extension of the well-known univariate GARCH, is one of the most useful tools in modeling the co-movement of multivariate time series with time-varying covariance matrix. Grounded on the review of various formulations of multivariate GARCH model, this paper estimates two MGARCH models, BEKK and DCC form, respectively, based on the data of three AAA-rated Euro zero-coupon bonds with different maturities (6 months/1 year/2 years). Post-model diagnostics indicates satisfying fitting performance of these estimated MGARCH models. Moreover, this paper provides comparison on the goodness of fit and forecasting performances of these forms by adopting the mean absolute error (MAE) criterion. Throughout this application, the conclusion can be drawn that significant fitting and forecasting performances originate from the trade-off between parsimony and flexibility of the MGARCH models.
KW - BEKK/DCC Form
KW - Multivariate GARCH Models
KW - Quasi-Maximum Likelihood Method
KW - Volatility
KW - Zero-Coupon Bonds
UR - https://www.scopus.com/pages/publications/78650224115
U2 - 10.1007/978-3-642-17313-4_10
DO - 10.1007/978-3-642-17313-4_10
M3 - 会议稿件
AN - SCOPUS:78650224115
SN - 3642173128
SN - 9783642173127
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 110
BT - Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
Y2 - 19 November 2010 through 21 November 2010
ER -