Porovnat metody

	Conditional Value-at-Risk (Expected Shortfall)	Model ARIMA (autoregresní integrovaný klouzavý průměr)	Exponential GARCH (EGARCH)	Kvantilová regrese
Obor	Finance	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model	Regression model
Rok vzniku	2000	2015	1991	1978
Tvůrce	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Box & Jenkins (Box-Jenkins methodology)	Nelson	Koenker & Bassett
Typ	Coherent tail-risk measure	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Conditional quantile regression
Původní zdroj	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	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.
Další názvy	CVaR, expected shortfall, average value-at-risk, tail VaR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon
Příbuzné	5	5	4	5
Shrnutí	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.