Modelling dynamic interdependence in nonstationary variances with an application to carbon markets

Susana Campos-Martins and Cristina Amado

Journal of Economic Dynamics and Control, 2025, vol. 173, issue C

Abstract: In this paper we propose a multivariate generalisation of the multiplicative decomposition of the volatility within the class of conditional correlation GARCH models. The GARCH variance equations are multiplicatively decomposed into a deterministic nonstationary component describing the long-run movements in volatility and a short-run dynamic component allowing for volatility interactions across markets or assets. The conditional correlations are assumed to be time-invariant in its simplest form or generalised into a flexible dynamic parameterisation. Parameters of the model are estimated equation-by-equation by maximum likelihood applying the maximisation by parts algorithm to the variance equations, and thereafter to the structure of conditional correlations. An empirical application using carbon markets data illustrates the usefulness of the model. Our results suggest that, after modelling the variance equations accordingly, we find evidence that the transmission mechanism of shocks is supported by the presence of dynamic interdependence in variances robust to nonstationarity.

Keywords: Variance interactions; Nonstationarity; Short and long-term volatility; Lagrange multiplier test (search for similar items in EconPapers)
JEL-codes: C12 C13 C32 C51 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:173:y:2025:i:c:s0165188925000284

DOI: 10.1016/j.jedc.2025.105062

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