Flexible and robust modelling of volatility comovements: a comparison of two multifractal models
Ruipeng Liu and
No 1594, Kiel Working Papers from Kiel Institute for the World Economy (IfW)
Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time series. In this paper, we propose a parsimonious version of a bivariate multifractal model and estimate its parameters via both maximum likelihood and simulation based inference approaches. In order to explore its practical performance, we apply the model for computing value-at-risk and expected shortfall statistics for various portfolios and compare the results with those from an alternative bivariate multifractal model proposed by Calvet et al. (2006) and the bivariate CC-GARCH of Bollerslev (1990). As it turns out, the multifractal models provide much more reliable results than CC-GARCH, and our new model compares well with the one of Calvet et al. although it has an even smaller number of parameters.
Keywords: Long memory; multifractal models; simulation based inference; value-at-risk; expected shortfall (search for similar items in EconPapers)
JEL-codes: C11 C13 G15 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwkwp:1594
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