Incorporating higher moments into value-at-risk forecasting

DOI10.1002/for.1155zbMath1204.91103OpenAlexW2034254534MaRDI QIDQ3065537

Evarist Stoja, Arnold Polanski

Publication date: 6 January 2011

Published in: Journal of Forecasting (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/for.1155

zbMATH Keywords

kurtosisskewnesstime-varying variancecharlier series expansiongramvalue-at-risk (VaR) forecasting

Mathematics Subject Classification ID

Statistical methods; economic indices and measures (91B82)

Related Items (7)

An Extensive Comparison of Some Well‐Established Value at Risk Methods ⋮ Efficient expressions for moments of dependent random sums using copulas ⋮ Gram-Charlier densities: maximum likelihood versus the method of moments ⋮ A detailed comparison of value at risk estimates ⋮ Elementary expressions for moments of truncated negative binomial random variables ⋮ Dynamic density forecasts for multivariate asset returns ⋮ Asymptotic theory for QMLE for the real‐time GARCH(1,1) model

Uses Software

• CAViaR
• RiskMetrics

Cites Work

• Conditional volatility, skewness, and kurtosis: Existence, persistence, and comovements
• A conditional extreme value volatility estimator based on high-frequency returns
• Prediction in dynamic models with time-dependent conditional variances
• Generalized autoregressive conditional heteroscedasticity
• Financial Data and the Skewed Generalized T Distribution
• Gram-Charlier densities.

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