Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation

Xin Du, Kai Moriyama, Kumiko Tanaka-Ishii

Abstract:

This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible
neural network. Since RV is known to be skewed and have a fat tail, previous methods transform RV into values that follow a
latent distribution with an explicit shape and then apply a prediction model. However, knowing that shape is non-trivial, and the
transformation result influences the prediction model. This paper proposes to jointly train the transformation and the prediction
model. The training process follows a maximum-likelihood objective function that is derived from the assumption that the prediction
residuals on the transformed RV time series are homogeneously Gaussian. The objective function is further approximated using an
expectation–maximum algorithm. On a dataset of 100 stocks, our method significantly outperforms other methods using analytical or
naïve neural-network transformations.