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| Mesures de risc de cua (Shortfall esperat, espectrals, expectils)× | Regressió per Mínims Quadrats Ordinàris (MQO)× | |
|---|---|---|
| Camp≠ | Finances | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1999 | 2019 |
| Autor original≠ | Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall) | Wooldridge (textbook treatment); classical least squares |
| Tipus≠ | Coherent tail risk measure | Linear regression |
| Font seminal≠ | Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Àlies≠ | expected shortfall, conditional value at risk, CVaR, spectral risk measure | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionats | 5 | 5 |
| Resum≠ | 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). |
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