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Non-homogeneous volatility correlations in the bivariate multifractal model

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  • Ruipeng Liu
  • Thomas Lux

Abstract

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. "Volatility Comovement: A Multifrequency Approach." Journal of Econometrics 131: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models." Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model.

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  • Ruipeng Liu & Thomas Lux, 2015. "Non-homogeneous volatility correlations in the bivariate multifractal model," The European Journal of Finance, Taylor & Francis Journals, vol. 21(12), pages 971-991, September.
    • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:12:p:971-991
    DOI: 10.1080/1351847X.2014.897960
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    Cited by:

    1. Lux, Thomas, 2022. "Inference for Nonlinear State Space Models: A Comparison of Different Methods applied to Markov-Switching Multifractal Models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 69-95.
    2. Lux, Thomas, 2018. "Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models," Economics Working Papers 2018-07, Christian-Albrechts-University of Kiel, Department of Economics.

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