It is an important task to make predictions of trading volumes of financial indices to market participants. In the present study, we focus on this issue for the Chinese Stock Index 300 (CSI300) spot by exploring the high-frequency one-minute data spanning the launch date of the corresponding...

This study compares the performances of neural network and Black-Scholes models in pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting approaches. Since the volatility is the key parameter in pricing options, GARCH (Generalized Autoregressive...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...