Weak Diffusion Limits of Dynamic Conditional Correlation Models

Christian Hafner, Sébastien Laurent and Francesco Violante ()

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Abstract: The properties of dynamic conditional correlation (DCC) models, introduced more than a decade ago, are still not entirely known. This paper fills one of the gaps by deriving weak diffusion limits of a modified version of the classical DCC model. The limiting system of stochastic differential equations is characterized by a diffusion matrix of reduced rank. The degeneracy is due to perfect collinearity between the innovations of the volatility and correlation dynamics. For the special case of constant conditional correlations, a nondegenerate diffusion limit can be obtained. Alternative sets of conditions are considered for the rate of convergence of the parameters, obtaining time-varying but deterministic variances and/or correlations. A Monte Carlo experiment confirms that the often used quasi-approximate maximum likelihood (QAML) method to estimate the diffusion parameters is inconsistent for any fixed frequency, but that it may provide reasonable approximations for sufficiently large frequencies and sample sizes.

Keywords: Economie; quantitative (search for similar items in EconPapers)
Date: 2017-06
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Citations: View citations in EconPapers (5)

Published in Econometric Theory, 2017, 33 (03), pp.691--716. ⟨10.1017/S0266466616000128⟩

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Related works:
Journal Article: WEAK DIFFUSION LIMITS OF DYNAMIC CONDITIONAL CORRELATION MODELS (2017)
Working Paper: Weak Diffusion Limits of Dynamic Conditional Correlation Models (2017)
Working Paper: Weak diffusion limits of dynamic conditional correlation models (2017)
Working Paper: Weak Diffusion Limits of Dynamic Conditional Correlation Models (2016)
Working Paper: Weak Diffusion Limits of Dynamic Conditional Correlation Models (2016)
Working Paper: Weak diffusion limits of dynamic conditional correlation models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01590010

DOI: 10.1017/S0266466616000128

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