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...

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose...

Determining which variables afect price realized volatility has always been challenging. This paper proposes to explain how fnancial assets infuence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine...