Abstract Machine learning approaches have become very popular in many fields in this big
data age. This paper considers the problem of forecasting realized volatility with machine …

This study evaluates TimesNet model for stock realized volatility forecasting, comparing its
efficacy against traditional and contemporary models across key metrics: RMSE, MAE …

We forecast the realized volatilities of China's agricultural commodity futures (corn, cotton,
palm, wheat, and soybean) using a set of multivariate heterogeneous autoregressive …

Forecast methods for realized volatilities are reviewed. Basic theoretical and empirical
features of realized volatilities as well as versions of estimators of realized volatility are …

Volatility prediction for financial assets is one of the essential questions for understanding
financial risks and quadratic price variation. However, although many novel deep learning …

The study determines if information extracted from a big data set that includes limit order
book (LOB) and Dow Jones corporate news can help to improve realised volatility …

Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities
such as risk management. The Econometrics and Machine Learning communities have …

This study evaluates the effectiveness of the TSMixer neural network model in forecasting
stock realized volatility, comparing it with traditional and contemporary benchmark models …

The increase in oil price volatility in recent years has raised the importance of forecasting it
accurately for valuing and hedging investments. The paper models and forecasts the crude …

This study examines the role of daily volatility persistence in transmitting information from
macro-economy in the volatility of energy markets. In crude oil and natural gas markets …