Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid

DOI10.1007/s00180-019-00934-7zbMath1505.62263arXiv2009.06910OpenAlexW3098049687WikidataQ126808834 ScholiaQ126808834MaRDI QIDQ2203392

Stefan Lessmann, Marius Lux, Wolfgang Karl Härdle

Publication date: 6 October 2020

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.06910

zbMATH Keywords

GARCHvalue-at-riskkernel density estimationsupport vector regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70)

Related Items (1)

Conditional quantile change test for time series based on support vector regression

Uses Software

• LIBSVM
• ElemStatLearn

Cites Work

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