Multivariate High-Frequency-Based Volatility (HEAVY) Models
Diaa Noureldin,
Neil Shephard and
Kevin Sheppard
No 533, Economics Series Working Papers from University of Oxford, Department of Economics
Abstract:
This paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences frommultivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations.
Keywords: HEAVY model; GARCH; multivariate volatility; realized covariance; covariance targeting; multi-step forecasting; Wishart distribution (search for similar items in EconPapers)
JEL-codes: C32 C52 C58 (search for similar items in EconPapers)
Date: 2011-02-01
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for, nep-mst and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)
Downloads: (external link)
https://ora.ox.ac.uk/objects/uuid:71273dd6-f379-4f8f-9fab-bc2989d4d58e (text/html)
Related works:
Journal Article: Multivariate high‐frequency‐based volatility (HEAVY) models (2012)
Working Paper: Multivariate High-Frequency-Based Volatility (HEAVY) Models (2011) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:533
Access Statistics for this paper
More papers in Economics Series Working Papers from University of Oxford, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Anne Pouliquen ( this e-mail address is bad, please contact ).