Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent the conditional distribution of asset returns. Across a wide range of asset classes, the out-of-sample forecasts of conditional quantiles or VaR of our model outperform the GARCH family. Further, the proposed approach does not suffer from the issue of quantile crossing, nor does it expose to the ill-posedness comparing to the parametric probability density function approach. Papers published at the Neural Information Processing Systems Conference.