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

Mawuli Segnon, Chi Keung Lau, Bernd Wilfling, Rangan Gupta

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number20190009
JournalStudies in Nonlinear Dynamics and Econometrics
Early online date17 Nov 2020
DOIs
Publication statusE-pub ahead of print - 17 Nov 2020

Bibliographical note

Publisher Copyright:
© 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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