Modelling volatility by variance decomposition

Cristina Amado () and Timo Teräsvirta

Journal of Econometrics, 2013, vol. 175, issue 2, 142-153

Abstract: In this paper, we propose two parametric alternatives to the standard GJR-GARCH model of Glosten et al. (1993), based on additive and multiplicative decompositions of the variance. They allow the variance of the model to have a smooth time-varying structure. The suggested parameterizations describe structural change in the conditional and unconditional variances where the transition between regimes over time is smooth. The main focus is on the multiplicative decomposition of the variance into an unconditional and conditional components. Estimation of the multiplicative model is discussed in detail. An empirical application to daily stock returns illustrates the functioning of the model. The results show that the ‘long memory type behaviour’ of the sample autocorrelation functions of the absolute returns can also be explained by deterministic changes in the unconditional variance.

Keywords: Conditional heteroskedasticity; Nonlinear time series; Maximum likelihood estimation; Iterative algorithm; Time-varying parameter model (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 C51 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (76)

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Related works:
Working Paper: Modelling Volatility by Variance Decomposition (2011)
Working Paper: Modelling Volatility by Variance Decomposition (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:175:y:2013:i:2:p:142-153

DOI: 10.1016/j.jeconom.2013.03.006

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