Comparar métodos

	Valor em Risco Condicional (Expected Shortfall)	Exponential GARCH (EGARCH)	Regressão Quantílica	Volatilidade Realizada e o Modelo HAR
Área	Finanças	Econometria	Econometria	Finanças
Família	Regression model	Regression model	Regression model	Regression model
Ano de origem	2000	1991	1978	2009
Autor original	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Nelson	Koenker & Bassett	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Tipo	Coherent tail-risk measure	Conditional volatility model (asymmetric GARCH variant)	Conditional quantile regression	Time-series regression of realized variance
Fonte seminal	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	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.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Outros nomes	CVaR, expected shortfall, average value-at-risk, tail VaR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Relacionados	5	4	5	5
Resumo	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.	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.	Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction.