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- W3126070107 abstract "We propose a scalar variation of the multivariate HEAVY model of Noureldin et al. which allows for a time-varying long run component in the specification of the daily conditional covariance matrix. Differently from the original model featuring a BEKK-type parameterization, ours extends it to allow for a separate modeling of the conditional volatilities and the conditional correlation matrix, in a DCC fashion. Estimation is performed in one step by QML and multi-step ahead forecasting is feasible applying the direct approach to the HEAVY-P equation. In an empirical application aiming at modeling and forecasting the conditional covariance matrix of a stock (BAC) and an index (S&P 500), we find that the new model statistically outperforms the original HEAVY model both in-sample and out-of-sample." @default.
- W3126070107 created "2021-02-01" @default.
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- W3126070107 date "2016-02-01" @default.
- W3126070107 modified "2023-09-27" @default.
- W3126070107 title "A time-varying long run HEAVY model" @default.
- W3126070107 hasPublicationYear "2016" @default.
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