Generalized Method of Moment estimation of multivariate multifractal models

Ruipeng Liu and Thomas Lux

Economic Modelling, 2017, vol. 67, issue C, 136-148

Abstract: Multifractal processes have recently been introduced as a new tool for modeling the stylized facts of financial markets and have been found to consistently provide certain gains in performance over basic volatility models for a broad range of assets and for various risk management purposes. Due to computational constraints, multivariate extensions of the baseline univariate multifractal framework are, however, still very sparse so far. In this paper, we introduce a parsimoniously designed multivariate multifractal model, and we implement its estimation via a Generalized Methods of Moments (GMM) algorithm. Monte Carlo studies show that the performance of this GMM estimator for bivariate and trivariate models is similar to GMM estimation for univariate multifractal models. An empirical application shows that the multivariate multifractal model improves upon the volatility forecasts of multivariate GARCH over medium to long forecast horizons.

Keywords: Multivariate; Multifractal; Long memory; GMM estimation (search for similar items in EconPapers)
JEL-codes: C20 G15 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:67:y:2017:i:c:p:136-148

DOI: 10.1016/j.econmod.2016.11.010

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