Evaluating Value-at-Risk Models via Quantile Regression

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Corporate Governance, Incentives, and Tax Avoidance (2015, background): “, 2001); the effect of education on women’s labor market value (Buchinsky, 2002); and to evaluate value-atrisk models (Gaglianone et al., 2011).”
Frontiers in VaR forecasting and backtesting (2016, methodology): “Furthermore, it is important to note that extensions of the methodology proposed by Gaglianone et al. (2011) to multivariate VaR forecast models and/or to ten-step-ahead VaRs are not straightforward.”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, unknown): “A further step was made by Osterrieder and Lorenz (2017) and Gkillas and Katsiampa (2018) to consider extreme value theory to estimate VaR. Other approaches take into account the time-varying volatility of cryptocurrencies, such as the performed by Ardia, Bluteau, and Rüede (2019), Stavroyiannis (20”
A joint quantile and expected shortfall regression framework (2017, unknown): “(2008), Gneiting (2011) and Fissler and Ziegel (2016), we introduce the concept of (multivariate) p-elicitability.”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “They propose the VQR (‘Value-at-Risk model based on quantile regressions’) test, which uses more information to reject a misspecified model and, hence, has more power in finite samples than the Christoffersen or the DQ tests.”