Advances in forecasting realized volatility: a review of methodologies

Abstract Over the past decade, volatility modeling has gained increasing importance in quantitative finance, significantly influencing risk management, investment strategies, and policymaking. Traditionally, academic research has emphasized linear models for forecasting realized volatility. However, advances in computational power have enabled the development of more sophisticated approaches, including machine and deep learning models. Despite these advancements, no comprehensive survey currently compares traditional linear methods with newer models in the context of realized volatility forecasting. This survey addresses that gap by analyzing all models used to forecast realized volatility in academic literature from 2000 to the first half of 2024. It highlights key academic contributions and examines the empirical characteristics of realized volatility. Our findings indicate that the top-performing hybrid convolutional neural network and long short-term memory model surpasses other models in forecasting accuracy. These results highlight the ability of such models to capture a broader range of factors driving realized volatility, affirming their valuable role in quantitative modeling.