Abstract
This paper presents an empirical application of the Multifractal Model of Asset Returns (MMAR) to intraday stock prices, with a goal of generating accurate volatility forecasts. Intraday stock volatility exhibits long tails, persistence, and strong evidence of moment scaling. This allows us to apply the MMAR. A forecasting method for the MMAR is implemented through Monte Carlo simulation, and this forecasting method is compared to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) alternatives over several testing samples. The MMAR significantly outperformed the GARCH models. This suggests that the framework of multifractality has a large potential for further development and application within finance.
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