Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
This work addresses the need for accurate risk management in finance by improving quantile forecasts for asset returns, though it appears incremental as it builds on existing sequential learning methods.
The authors tackled the problem of forecasting dynamic tail behaviors of financial asset returns by proposing a parsimonious quantile regression framework that combines LSTM with a novel parametric quantile function, resulting in out-of-sample forecasts that outperform the GARCH family across various asset classes.
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. 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. Our model 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 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.