A DCC-type approach for realized covariance modeling with score-driven dynamics
| Autor: | Fulvio Corsi, Danilo Vassallo, Giuseppe Buccheri |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Wishart distribution
Settore SECS-S/03 Realized variance Estimation theory Settore SECS-P/05 05 social sciences Covariance forecasting HB Univariate Dynamic dependencies Settore SECS-S/06 Covariance Matrix (mathematics) Estimation errors Dimension (vector space) Simple (abstract algebra) Realized covariance Score-driven models 0502 economics and business Applied mathematics 050207 economics Business and International Management 050205 econometrics Mathematics |
| ISSN: | 0169-2070 |
| Popis: | We propose a class of score-driven realized covariance models where volatilities and correlations are separately estimated. We can thus combine univariate realized volatility models with a recently introduced class of score-driven realized covariance models based on Wishart and matrix- F distributions. Compared to the latter, the proposed models remain computationally simple at high dimensions and allow for higher flexibility in parameter estimation. Through a Monte-Carlo study, we show that the two-step maximum likelihood procedure provides accurate parameter estimates in small samples. Empirically, we find that the proposed models outperform those based on joint estimation, with forecasting gains that become more significant as the cross-section dimension increases. |
| Databáze: | OpenAIRE |
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