Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

We propose a parsimonious quantile regression framework to learn the dynamic tail behavior of financial asset returns. Our method captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of the popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent conditional distribution of asset returns. Our method also captures individually the serial dependences of higher moments, rather than just the volatility. 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 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.