A dynamic component model for forecasting high-dimensional realized covariance matrices
Luc Bauwens (),
Manuela Braione and
Giuseppe Storti ()
Econometrics and Statistics, 2017, vol. 1, issue C, 40-61
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long term dynamics in the conditional (co)volatilities of asset returns, in line with the empirical evidence suggesting that their level is changing over time as a function of economic conditions. Herein the applicability of the model is improved along two directions. First, by proposing an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function and keeps estimation feasible in large dimensions by mitigating the incidental parameter problem. Second, by illustrating a conditional bootstrap procedure to generate multi-step ahead predictions from the model. In an empirical application on a dataset of forty-six equities, the MMReDCC model is found to statistically outperform the selected benchmarks in terms of in-sample fit as well as in terms of out-of-sample covariance predictions. The latter are mostly significant in periods of high market volatility.
Keywords: Realized covariance; dynamic component models; multi-step forecasting; iterative algorithm (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only. Contains open access articles
Working Paper: A dynamic component model for forecasting high-dimensional realized covariance matrices (2017)
Working Paper: A dynamic component model for forecasting high-dimensional realized covariance matrices (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:1:y:2017:i:c:p:40-61
Access Statistics for this article
Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi
More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().