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Contribution Details
| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data |
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| Institution | University of Zurich |
| Series Name | - |
| Number | - |
| Date | 2020 |
| Abstract Text | Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did 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 (O/H/L/C) price data instead of simply using daily returns. A key innovation is the concept of a synthetic return, which is obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return. |
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