A threshold autoregressive model for wholesale electricity prices

We introduce a discrete-time model for electricity prices, which accounts for both transitory spikes and temperature effects. The model allows for different rates of mean-reversion: One for weather events, one around price jumps, and another for the remainder of the process. We estimate the model using a Markov chain Monte Carlo approach with three years of data from Allegheny County, Pennsylvania. We show that our model outperforms existing stochastic jump-diffusion models for this data set. Results also demonstrate the importance of model parameters corresponding to both the temperature effect and the multi-level mean-reversion rate.

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