Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model

DOI10.1080/02664763.2019.1584161OpenAlexW2916986870WikidataQ128307856 ScholiaQ128307856MaRDI QIDQ5034154

Mohamed El Ghourabi, Imed Gamoudi, Asma Nani

Publication date: 24 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2019.1584161

zbMATH Keywords

asymmetric Laplace distributionconditional risksparsenessgeneralized error distributionartificial intelligence models

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Generalized autoregressive conditional heteroscedasticity
• Weighted least squares support vector machines: robustness and sparse approximation
• Complete statistical theory of learning
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
• Predict GARCH Based Volatility of Shanghai Composite Index by Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines
• On conditional risk estimation considering model risk

This page was built for publication: Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model