Multivariate range-based EGARCH models

Lili Yan, Neil M. Kellard and Lyudmyla Lambercy

International Review of Financial Analysis, 2025, vol. 100, issue C

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.

Keywords: Range-based covariance forecasting; EGARCH; DCC; EWMA; Portfolio modelling (search for similar items in EconPapers)
JEL-codes: C53 C58 G11 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:100:y:2025:i:c:s1057521925000705

DOI: 10.1016/j.irfa.2025.103983

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