Capturing deep tail risk via sequential learning of quantile dynamics

DOI10.1016/J.JEDC.2019.103771zbMath1425.91435OpenAlexW2966027283WikidataQ127187277 ScholiaQ127187277MaRDI QIDQ2007859

Qi Wu, Xing Yan

Publication date: 22 November 2019

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jedc.2019.103771

zbMATH Keywords

neural networkmachine learningfinancial risk managementlong short-term memoryasymmetric heavy-tail distributiondynamic quantile modelingparametric quantile functionstime-varying higher-order conditional momentsVaR forecasts

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Statistics of extreme values; tail inference (62G32)

Uses Software

• CAViaR
• TensorFlow
• Adam

Cites Work

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• What good is a volatility model?
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