Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model

Abstract

Previously, application of deep learning based sequential model drastically improved accuracy of volatility prediction in modelling of financial time series. However, unlike traditional financial time series model such as GARCH family of models, majority of deep learning based financial time series models focus solely on optimizing a single-step volatility prediction error and are not capable of conduct multi-step training and prediction of volatilities since volatility is the inherent uncertainty of the model prediction, whose multi-step prediction is drastically different from prediction of the mean of the financial time series.