Сравнение методов

	Tail Risk Measures	Квантильная регрессия
Область	Финансы	Эконометрика
Семейство	Regression model	Regression model
Год появления	1999	1978
Автор метода	Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)	Koenker & Bassett
Тип	Coherent tail risk measure	Conditional quantile regression
Основополагающий источник	Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Другие названия	expected shortfall, conditional value at risk, CVaR, spectral risk measure	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	5	5
Сводка	Tail risk measures quantify the loss distribution beyond Value-at-Risk (VaR). Expected Shortfall — the expected loss given that VaR is exceeded — is the leading coherent risk measure, formalised by Artzner, Delbaen, Eber and Heath (1999) and shown to be coherent by Acerbi and Tasche (2002). Spectral and expectile-based measures generalise it.	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.