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This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the Engle (2002) DCC model and Shephard and Sheppard (2012) HEAVY model. We discuss the models' dynamics and highlight their differences from DCC-GARCH models. Specifically, the dynamics of conditional variances are driven by the lagged realized variances, while the dynamics of conditional correlations are driven by the lagged realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed-form formulas for multi-step forecasts. Empirical results suggest that the DCC-HEAVY model outperforms the DCC-GARCH model in and out-of-sample. |
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Xu, Yongdeng VerfasserIn (DE-588)1034374370 (DE-627)745440010 (DE-576)381993655 aut, DCC-HEAVY a multivariate GARCH model with realized measures of variance and correlation Yongdeng Xu, Cardiff, United Kingdom Cardiff Business School, Cardiff University February 2019, 1 Online-Ressource (circa 26 Seiten) Illustrationen, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Cardiff economics working papers no. E2019, 5, This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the Engle (2002) DCC model and Shephard and Sheppard (2012) HEAVY model. We discuss the models' dynamics and highlight their differences from DCC-GARCH models. Specifically, the dynamics of conditional variances are driven by the lagged realized variances, while the dynamics of conditional correlations are driven by the lagged realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed-form formulas for multi-step forecasts. Empirical results suggest that the DCC-HEAVY model outperforms the DCC-GARCH model in and out-of-sample., Cardiff economics working papers no. E2019, 5 2019,5 (DE-627)519767187 (DE-576)281324964 (DE-600)2257349-5 1749-6101, http://carbsecon.com/wp/E2019_5.pdf Verlag kostenfrei Volltext, http://carbsecon.com/wp/E2019_5_appendix.pdf Verlag kostenfrei Volltext, http://hdl.handle.net/10419/230438 Resolving-System kostenfrei, http://carbsecon.com/wp/E2019_5_appendix.pdf LFER, LFER 2019-05-29T00:00:00Z |
| spellingShingle |
Xu, Yongdeng, DCC-HEAVY: a multivariate GARCH model with realized measures of variance and correlation, Cardiff economics working papers, no. E2019, 5, This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the Engle (2002) DCC model and Shephard and Sheppard (2012) HEAVY model. We discuss the models' dynamics and highlight their differences from DCC-GARCH models. Specifically, the dynamics of conditional variances are driven by the lagged realized variances, while the dynamics of conditional correlations are driven by the lagged realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed-form formulas for multi-step forecasts. Empirical results suggest that the DCC-HEAVY model outperforms the DCC-GARCH model in and out-of-sample. |
| title |
DCC-HEAVY: a multivariate GARCH model with realized measures of variance and correlation |
| title_auth |
DCC-HEAVY a multivariate GARCH model with realized measures of variance and correlation |
| title_full |
DCC-HEAVY a multivariate GARCH model with realized measures of variance and correlation Yongdeng Xu |
| title_fullStr |
DCC-HEAVY a multivariate GARCH model with realized measures of variance and correlation Yongdeng Xu |
| title_full_unstemmed |
DCC-HEAVY a multivariate GARCH model with realized measures of variance and correlation Yongdeng Xu |
| title_in_hierarchy |
no. E2019, 5. DCC-HEAVY: a multivariate GARCH model with realized measures of variance and correlation (February 2019) |
| title_short |
DCC-HEAVY |
| title_sort |
dcc heavy a multivariate garch model with realized measures of variance and correlation |
| title_sub |
a multivariate GARCH model with realized measures of variance and correlation |
| url |
http://carbsecon.com/wp/E2019_5.pdf, http://carbsecon.com/wp/E2019_5_appendix.pdf, http://hdl.handle.net/10419/230438 |
