قارن الطرق

	مقاييس مخاطر الذيل (النقص المتوقع، الطيفية، التوقعات)	انحدار المربعات الصغرى العادية (OLS)
المجال	التمويل	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	1999	2019
صاحب الطريقة	Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)	Wooldridge (textbook treatment); classical least squares
النوع	Coherent tail risk measure	Linear regression
المصدر التأسيسي	Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
الأسماء البديلة	expected shortfall, conditional value at risk, CVaR, spectral risk measure	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5
الملخص	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).