European call options using a constructed implied volatility surface. The prices generated by the ANN were compared to the …

Forecasts of stock market volatility is an important input for market participants in measuring and managing investment … Machine Learning methods, and specifically Artificial Neural Network (ANN) models to forecast volatility. The ANN models are … measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the …

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility … realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that … machine learning methods can capture the stylized facts about volatility without relying on any assumption about the …

Bitcoin's price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non … volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of … Bitcoin's price volatility. …