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

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

No 6117, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

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
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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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