Parsimonious Quantile Regression Of Financial Asset Tail Dynamics Via Sequential Learning

Authors:
Xing Yan The Chinese University of Hong Kong
Weizhong Zhang Zhejiang University
Lin Ma Tencent AI Lab
Wei Liu Tencent AI Lab
Qi Wu City University of Hong Kong

Introduction:

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

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.

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