Extreme-Quantile Tracking for Financial Time Series

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Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution (2019, methodology): “Semiparametric approaches to VaR forecasting include those that use extreme value theory (EVT; see, e.g., Chavez-Demoulin, Embrechts, and Sardy 2014), and those that directly model the conditional quantile for a chosen probability level using quantile regression, such as conditional autoregressive…”
Statistics and Quantitative Risk Management for Banking and Insurance (2014, methodology): “More recent statistical techniques in use are for instance extreme quantile estimation based on regime-switching models and lasso-technology, see Chavez-Demoulin et al. (2013a).”
Extreme M-quantiles as risk measures: From $L^{1}$ to $L^{p}$ optimization (2019, unknown): “τ 1 npp, αn; 2q E |X qαnp2q| p 11ItX¤qαn p2qu E r|X qαnp2q| 1s (14)”