विधियों की तुलना करें

	पूंछ जोखिम माप (अपेक्षित अल्पता, स्पेक्ट्रल, एक्सपेक्टाइल)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	वित्त	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1999	2019
प्रवर्तक	Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)	Wooldridge (textbook treatment); classical least squares
प्रकार	Coherent tail risk measure	Linear regression
मौलिक स्रोत	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
उपनाम	expected shortfall, conditional value at risk, CVaR, spectral risk measure	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	5
सारांश	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).