Improving the value at risk forecasts: theory and evidence from the financial crisis

DOI10.1016/J.JEDC.2011.10.005zbMath1345.91084OpenAlexW3124459826MaRDI QIDQ310964

Winfried Pohlmeier, Roxana Halbleib

Publication date: 28 September 2016

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

Full work available at URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-290119

zbMATH Keywords

quantile regressionvalue-at-riskmethod of momentsfinancial crisisoptimal forecast combination

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70)

Related Items (6)

Quantifying market risk with value-at-risk or expected shortfall? -- Consequences for capital requirements and model risk ⋮ Improving daily value-at-risk forecasts: the relevance of short-run volatility for regulatory quality assessment ⋮ Information content of liquidity and volatility measures ⋮ Measurement of bivariate risks by the north-south quantile points approach ⋮ A joint quantile and expected shortfall regression framework ⋮ High frequency-based quantile forecast and combination: an application to oil market

Uses Software

• CAViaR

Cites Work

• Unnamed Item
• Global optimization of statistical functions with simulated annealing
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Regression Quantiles

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