Model selection based on value-at-risk backtesting approach for GARCH-type models

DOI10.3934/jimo.2019021zbMath1449.62239OpenAlexW2921387330WikidataQ128137449 ScholiaQ128137449MaRDI QIDQ2190298

You-Beng Koh, Kooi-Huat Ng, Hao-Zhe Tay, Kok-Haur Ng

Publication date: 18 June 2020

Published in: Journal of Industrial and Management Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/jimo.2019021

zbMATH Keywords

backtestmodel selectionvalue-at-riskGARCH modelskewed distribution

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Economic time series analysis (91B84)

Uses Software

• CAViaR

Cites Work

• Properties and estimation of asymmetric exponential power distribution
• Asymmetric GARCH processes featuring both threshold effect and bilinear structure
• A generalized asymmetric Student-\(t\) distribution with application to financial econometrics
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• BL-GARCH models and asymmetries in volatility
• Generalized autoregressive conditional heteroscedasticity
• Modeling the dynamics of interest rate volatility with skewed fat-tailed distributions
• The use of GARCH models in VaR estimation
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• On Bayesian Modeling of Fat Tails and Skewness
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Quadratic ARCH Models
• A review of backtesting for value at risk
• Value-at-Risk Prediction: A Comparison of Alternative Strategies
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