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

Michael Stanley Smith ()
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Michael Stanley Smith: University of Melbourne, Melbourne Business School

A chapter in Statistical Modelling and Regression Structures, 2010, pp 353-376 from Springer

Abstract: 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.

Keywords: Periodic Autoregression; Bayesian MCMC; Electricity Price Forecasting; Multivariate Stochastic Volatility; Vector Autoregression; LongitudinalModel; Heteroscedasticity (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2413-1_19

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DOI: 10.1007/978-3-7908-2413-1_19

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