Abstract
Deregulation of energy markets has led to increase volatilities in energy prices. Cost of investments are therefore quite huge for market participants, and thus require prudent investment decisions. This paper therefore employs the recently proposed state of the art network modelling techniques known as the Bayesian graphical vector autoregressive (BG–VAR) model to examine the complex network dynamics in zonal power market movements. In addition, it accommodates the statistical and computational challenges associated with inference of interdependence (or temporal dependence) from observed multivariate time series. Our findings show the relevance of this methodology in studying interconnectedness, extracting useful hidden spatial information. In particular, various network measures have been explored in view of systemic risk spread, which therefore provide a benchmark for proper energy risk management as well as ensuring energy supply reliability and security.
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