Are Multifractal Processes Suited to Forecasting Electricity Price Volatility? Evidence from Australian Intraday Data

Mawuli Segnon, Chi Keung Lau, Bernd Wilfling and Rangan Gupta
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Mawuli Segnon: Westfälische Wilhelms-Universität Münster, Department of Economics (CQE), Germany and Mark E AG, Germany

No 201739, Working Papers from University of Pretoria, Department of Economics

Abstract: We analyze Australian electricity price returns and find that they exhibit multifractal structures. Consequently, we let the return mean equation follow a long memory smooth transition autoregressive (STAR) process and specify volatility dynamics as a Markov-switching multifractal (MSM) process. We compare the out-of-sample volatility forecasting performance of the STAR-MSM model with that of other STAR mean processes, combined with various conventional GARCH-type volatility equations (for example, STAR-GARCH(1,1)). We find that the STAR-MSM model competes with conventional STAR-GARCH specifications with respect to volatility forecasting, but does not (systematically) outperform them.

Keywords: Electricity price volatility; multifractal modeling; GARCH processes; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2017-05
New Economics Papers: this item is included in nep-dcm, nep-ene, nep-for and nep-ore
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Related works:
Journal Article: Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data (2022)
Working Paper: Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data (2017)
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