The behaviour of time series data from financial markets is influenced by a rich mixture of
quantitative information from the dynamics of the system, captured in its past behaviour, and …

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 …

This paper proposes an empirical similarity approach to forecast weekly volatility by using
search engine data as a measure of investors attention to the stock market index. Our model …

The ability to forecast the volatility of the markets is critical to analysts. Among the large array
of approaches available for forecasting volatility, neural networks are gaining in popularity …

We examine the predictive power of machine learning (ML) models for forecasting realised
volatility (RV) using different data sets tested in the literature, viz., variables from the HAR …

We investigate the impact of news sentiment on the price dynamics of natural gas futures.
We propose a Local News Sentiment Level model, based on the Local Level model of …

We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when
forecasting realized volatility in the WTI futures market. Additionally, we study whether other …

This study explores the effect of investor sentiment on the volatility forecasting power of
option-implied information. We find that the risk-neutral skewness has the explanatory power …

Financial market volatility is an important input for investment, option pricing and financial
market regulation. In this review article, we compare the volatility forecasting findings in 72 …

Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the
information flow, we claim that cross-market volatility transmission effects are synonymous to …