Сравнение методов

	Условный риск (Expected Shortfall)	Экспоненциальный GARCH (EGARCH)	Квантильная регрессия
Область	Финансы	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	2000	1991	1978
Автор метода	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Nelson	Koenker & Bassett
Тип	Coherent tail-risk measure	Conditional volatility model (asymmetric GARCH variant)	Conditional quantile regression
Основополагающий источник	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Другие названия	CVaR, expected shortfall, average value-at-risk, tail VaR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	5	4	5
Сводка	Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.