We inspect how accurate machine learning (ML) is at forecasting realized variance of the
Dow Jones Industrial Average index constituents. We compare several ML algorithms …

This paper comprehensively examines the connection between oil futures volatility and the
financial market based on a model-rich environment, which contains traditional predicting …

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting
commonality in intraday volatility via pooling stock data together, and by incorporating a …

We employ deep learning in forecasting high‐frequency returns at multiple horizons for 115
stocks traded on Nasdaq using order book information at the most granular level. While raw …

In a novel take on the gradual information diffusion hypothesis of Hong et al.(2007), we
examine the predictive role of industries over aggregate stock market volatility. Using high …

Financial economics and econometrics literature demonstrate that the limit order book data
is useful in predicting short-term volatility in stock markets. In this paper, we are interested in …

This paper aims to address the predictor choice issue in forecasting volatility of INE oil
futures by a comprehensive comparative study with a large number of predictive variables …

We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks
aiming to forecast the next daily realized volatility for all stocks. Showing the consistent …

This study develops FinText, a financial word embedding compiled from 15 years of
business news archives. The results show that FinText produces substantially more accurate …

Despite the impressive success of deep neural networks in many application areas, neural
network models have so far not been widely adopted in the context of volatility forecasting. In …