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Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices

In: Statistical Modelling and Regression Structures

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  • Michael Stanley Smith

    (University of Melbourne, Melbourne Business School)

Abstract

The Gaussian stochastic volatility model is extended to allow for periodic autoregressions (PAR) in both the level and log-volatility process. Each PAR is represented as a first order vector autoregression for a longitudinal vector of length equal to the period. The periodic stochastic volatility model is therefore expressed as a multivariate stochastic volatility model. Bayesian posterior inference is computed using a Markov chain Monte Carlo scheme for the multivariate representation. A circular prior that exploits the periodicity is suggested for the log-variance of the log-volatilities. The approach is applied to estimate a periodic stochastic volatility model for half-hourly electricity prices with period m = 48. Demand and day types are included in both the mean and log-volatility equations as exogenous effects. A nonlinear relationship between demand and mean prices is uncovered which is consistent with economic theory, and the predictive density of prices evaluated over a horizon of one week. Overall, the approach is shown to have strong potential for the modelling of periodic heteroscedastic data.

Suggested Citation

  • Michael Stanley Smith, 2010. "Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices," Springer Books, in: Thomas Kneib & Gerhard Tutz (ed.), Statistical Modelling and Regression Structures, pages 353-376, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2413-1_19
    DOI: 10.1007/978-3-7908-2413-1_19
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