Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations

Annastiina Silvennoinen and Timo Teräsvirta

No 168, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

Abstract: In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into correlations and standard deviations. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to an endogenous or exogenous transition variable. An LM test is derived to test the constancy of correlations and LM and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the Standard & Poor 500 stock index completes the paper. The model is estimated for the full five-dimensional system as well as several subsystems and the results discussed in detail.

Keywords: multivariate GARCH; constant conditional correlation; dynamic conditional correlation; return comovement; variable correlation GARCH model; volatility model evaluation (search for similar items in EconPapers)
JEL-codes: C12 C32 C51 C52 G1 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2005-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (64)

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https://www.uts.edu.au/sites/default/files/qfr-archive-02/QFR-rp168.pdf (application/pdf)

Related works:
Working Paper: Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:168

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