Reviews: Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

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.