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Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data

Author

Listed:
  • Segnon Mawuli

    (Westfälische Wilhelms-Universität, Münster, Germany)

  • Lau Chi Keung

    (University of Huddersfield, Huddersfield, UK)

  • Wilfling Bernd

    (Westfälische Wilhelms-Universität, Münster, Germany)

  • Gupta Rangan

    (University of Pretoria, Pretoria, South Africa)

Abstract

We analyze Australian electricity price returns and find that they exhibit volatility clustering, long memory, structural breaks, and multifractality. Consequently, we let the return mean equation follow two alternative specifications, namely (i) a smooth transition autoregressive fractionally integrated moving average (STARFIMA) process, and (ii) a Markov-switching autoregressive fractionally integrated moving average (MSARFIMA) process. We specify volatility dynamics via a set of (i) short- and long-memory GARCH-type processes, (ii) Markov-switching (MS) GARCH-type processes, and (iii) a Markov-switching multifractal (MSM) process. Based on equal and superior predictive ability tests (using MSE and MAE loss functions), we compare the out-of-sample relative forecasting performance of the models. We find that the (multifractal) MSM volatility model keeps up with the conventional GARCH- and MSGARCH-type specifications. In particular, the MSM model outperforms the alternative specifications, when using the daily squared return as a proxy for latent volatility.

Suggested Citation

  • Segnon Mawuli & Lau Chi Keung & Wilfling Bernd & Gupta Rangan, 2022. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 73-98, February.
    • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:1:p:73-98:n:3
    DOI: 10.1515/snde-2019-0009
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    More about this item

    Keywords

    electricity price volatility; GARCH-type processes; Markov-switching processes; multifractal modeling; volatility forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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