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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Large dynamic covariance matrices: Enhancements based on intraday data |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Journal of Banking and Finance |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0378-4266 |
| Volume | 138 |
| Page Range | 106426 |
| Date | 2022 |
| Abstract Text | Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return. |
| Free access at | Official URL |
| Official URL | https://doi.org/10.1016/j.jbankfin.2022.106426 |
| Digital Object Identifier | 10.1016/j.jbankfin.2022.106426 |
| Other Identification Number | merlin-id:22981 |
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