Bayesian inference for a periodic stochastic volatility model of intraday electricity prices

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. © 2010 Springer-Verlag Berlin Heidelberg.