Multivariate volatility modeling of electricity futures

Bauwens, Luc;Hafner, Christian;Pierret, Diane
(2013) Journal of Applied Econometrics — Vol. 28, n° 5, p. 743-761 (2013)

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Authors
  • Bauwens, Lucorcid-logoUCLouvain
    Collaborator
  • Hafner, ChristianUCLouvain
    Author
  • Pierret, DianeUCLouvain
    Author
Abstract
We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long-run from short-run components. We allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate nonparametrically. For the short-run dynamics, we use a GJR-GARCH model for the conditional variances and augmented DCC models for the conditional correlations. We also introduce exogenous variables to account for congestion and delivery date effects in short-term conditional variances. We find different correlation dynamics for long- and short-term contracts and the new model achieves higher forecasting performance compared to a standard DCC model.
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Hafner, C., & Pierret, D. (2013). Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics, 28(5), 743-761. https://doi.org/10.1002/jae.2280 (Original work published 2013)