Yan, Lili, Kellard, Neil M. and Lambercy, Lyudmyla (2025) Multivariate range-based EGARCH models. International Review of Financial Analysis (IRFA), 100:103983. ISSN 1057-5219 (Print), 1873-8079 (Online) (doi:10.1016/j.irfa.2025.103983)

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Abstract

The dynamic conditional correlation (DCC) and co-range models are two main frameworks used to incorporate range-based univariate volatility. Using the two approaches, we construct novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, and a co-range CARR (CRCARR) model. We compare these models with five existing models over twelve forecast horizons, ranging from one to twelve weeks, covering currencies and ETFs. Among the eight models, the DCC-REGARCH and CRREGARCH models show the best performance in out-of-sample forecasting of the variance-covariance matrix across a range of market conditions and forecast horizons. These models also generate the lowest variance and turnover for global minimum-variance (GMV) portfolios in the majority of cases.

Item Type:	Article
Uncontrolled Keywords:	range-based covariance forecasting, EGARCH,DCC, EWMA, portfolio modelling
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > Political Economy, Governance, Finance and Accountability (PEGFA) Journal of Economic Literature Classification > Political Economy, Governance, Finance and Accountability (PEGFA) Greenwich Business School > School of Accounting, Finance and Economics
Last Modified:	12 Feb 2025 16:38
URI:	http://gala.gre.ac.uk/id/eprint/49715

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