Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter ArXiv ID: 2311.04727 “View on arXiv” Authors: Unknown Abstract We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the “crypto-winter” in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process. ...