We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.

Summary This paper describes an approach to learning the dynamics of financial time series. The authors describe a parametric quantile function with four parameters (modelling location, scale, and the shapes of the left and right hand tails of the conditional distribution of returns). The time dynamics of these parameters are learned using LSTM neural network. The performance of the algorithm is compared to various GARCH-type specifications and a TQR model (which combines "traditional" quantile regression with a LTSM neural network). Strengths I enjoyed reading the paper.

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.