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

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  • Campos-Martins, Susana
  • Amado, Cristina

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.

Suggested Citation

  • Campos-Martins, Susana & Amado, Cristina, 2025. "Modelling dynamic interdependence in nonstationary variances with an application to carbon markets," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:dyncon:v:173:y:2025:i:c:s0165188925000284
    DOI: 10.1016/j.jedc.2025.105062
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    More about this item

    Keywords

    Variance interactions; Nonstationarity; Short and long-term volatility; Lagrange multiplier test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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