A time-varying long run HEAVY model

Manuela Braione

No 2016002, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Keywords: HEAVY model; Long term models; Mixed Data Sampling; Direct forecasting (search for similar items in EconPapers)
Date: 2016-02-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (2)

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Journal Article: A time-varying long run HEAVY model (2016)
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