Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

Abstract

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.

arXiv commentNeurIPS 2018:1582-1592

Cited by in corpus (5)

• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
• A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface
• Cross-sectional Learning of Extremal Dependence among Financial Assets
• Regularization Strategies for Quantile Regression
• Generative Learning of Heterogeneous Tail Dependence