 Forecasting Value-at-Risk and Expected Shortfall using penalized quantile regressions with mixed-frequency dataLu Li, Degao Li, Li Liu and Linjun Tang The North American Journal of Economics and Finance, 2025, vol. 80, issue C Abstract: Value-at-Risk (VaR) and Expected Shortfall (ES), as two essential tools for risk management, have received widespread attention for their ability to provide financial institutions with a quantitative measure of potential losses. This paper considers a mixed-frequency quantile regression model to enhance the accuracy of VaR and ES predictions. We propose a multi-step estimation procedure based on penalized quantile regression methods and establish a goodness-of-fit test using a bootstrap approach. Simulation studies demonstrate that the Elastic Net penalized quantile regression performs well in identifying significant lags in time series data with high correlations, and our proposed bootstrap testing approach performs effectively. Empirical results from three representative Asian stock markets indicate that our methods achieve high accuracy in VaR and ES predictions. Keywords: Value-at-Risk; Expected shortfall; Penalized quantile regression; Mixed-frequency data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:80:y:2025:i:c:s1062940825001068 DOI: 10.1016/j.najef.2025.102466 Access Statistics for this article The North American Journal of Economics and Finance is currently edited by Hamid Beladi More articles in The North American Journal of Economics and Finance from Elsevier |
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