About this document
RV Pred Oxford by harsh.iitd is a document available to read on EtoBox.
This article explores the use of machine learning models to forecast intraday realized volatility (RV) by leveraging commonality in volatility across stocks and incorporating market volatility proxies. The study finds that neural networks outperform traditional models, demonstrating robust performance even on new stocks, and introduces a novel approach for predicting daily volatility using past intraday RVs. The results highlight the importance of high-frequency data and commonality in improving forecasting
- Author
- harsh.iitd
- Language
- EN