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• W2120590711 abstract "We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. We account for possible fat-failedness of both type of data by assuming a matrix-F distribution for the realized kernels, and a multivariate Student’s t distribution for the returns. Using score dynamics for the unobserved true covariance matrix, our approach automatically corrects for the effect of outliers and incidentally large observations, both in returns and in realized (co)variances. By an appropriate choice of scaling of the conditional score function we retain a convenient matrix formulation for the dynamic updates of the covariance matrix. This makes the model highly computationally efficient. The model performs well for both simulated and empirical data. In our empirical application, we study daily returns and realized kernels from 30 equities over the period 2001-2014 and find that the new model statistically outperforms (recently developed) multivariate volatility models, both in-sample and out-of-sample. We also comment on the possibility of composite likelihood methods for estimation." @default.
• W2120590711 created "2016-06-24" @default.
• W2120590711 creator A5019772497 @default.
• W2120590711 creator A5041348567 @default.
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• W2120590711 date "2014-01-01" @default.
• W2120590711 modified "2023-09-24" @default.
• W2120590711 title "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels" @default.
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• W2120590711 doi "https://doi.org/10.2139/ssrn.2457024" @default.
• W2120590711 hasPublicationYear "2014" @default.
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