Publication: Equation-by-Equation Estimation of Multivariate Periodic Electricity Price Volatility
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2016-07
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Abstract
Electricity prices are characterised by strong autoregressive persistence, periodicity (e.g.
intraday, day-of-the week and month-of-the-year effects), large spikes or jumps, GARCH and
-as evidenced by recent findings- periodic volatility. We propose a multivariate model of
volatility that decomposes volatility multiplicatively into a non-stationary (e.g. periodic) part
and a stationary part with log-GARCH dynamics. Since the model belongs to the log-GARCH
class, the model is robust to spikes or jumps, allows for a rich variety of volatility dynamics
without restrictive positivity constraints, can be estimated equation-by-equation by means of
standard methods even in the presence of feedback, and allows for Dynamic Conditional
Correlations (DCCs) that can –optionally- be estimated subsequent to the volatilities. We use
the model to study the hourly day-ahead system prices at Nord Pool, and find extensive
evidence of periodic volatility and volatility feedback. We also find that volatility is
characterised by (positive) leverage in half of the hours, and that a DCC model provides a
better fit of the conditional correlations than a Constant Conditional Correlation (CCC) model.
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Electricity prices, financial return, volatility, ARCH, exponential GARCH, log-GARCH, Multivariate GARCH, Dynamic Conditional Correlations, inverse leverage, Nord Pool