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.

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多重分形过程是否适合预测电价波动？来自澳大利亚日内数据的证据

我们分析了澳大利亚的电价回报，发现它们表现出多重分形结构。因此，我们让回归均值方程遵循长记忆平滑过渡自回归 (STAR) 过程，并将波动率动态指定为马尔可夫切换多重分形 (MSM) 过程。我们将 STAR-MSM 模型的样本外波动率预测性能与其他 STAR 平均过程的性能进行比较，并结合各种传统的 GARCH 型波动率方程（例如，STAR-GARCH(1,1)）。我们发现 STAR-MSM 模型在波动率预测方面与传统的 STAR-GARCH 规范竞争，但并没有（系统地）优于它们。