Comparison of value-at-risk models using the MCS approach

DOI10.1007/s00180-016-0646-6zbMath1342.65020OpenAlexW2285445848MaRDI QIDQ736648

Mauro Bernardi, Leopoldo Catania

Publication date: 4 August 2016

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

Full work available at URL: https://doi.org/10.1007/s00180-016-0646-6

zbMATH Keywords

GAShypothesis testingARCHvalue-at-riskmodel confidence setCAViaR modelsVaR combination

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) 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)

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Modeling returns volatility: realized GARCH incorporating realized risk measure ⋮ Forecasting volatility returns of oil price using gene expression programming approach.

Uses Software

• R
• MCS
• CRAN
• CAViaR
• MSC
• DySco

Cites Work

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