Comparar métodos

	Valor en Riesgo Condicional (Exceso de Pérdidas Esperadas)	Modelo ARIMA (Autoregressive Integrated Moving Average)	Volatilidad Realizada y el Modelo HAR
Campo	Finanzas	Econometría	Finanzas
Familia	Regression model	Regression model	Regression model
Año de origen	2000	2015	2009
Autor original	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Box & Jenkins (Box-Jenkins methodology)	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Tipo	Coherent tail-risk measure	Univariate time-series model	Time-series regression of realized variance
Fuente seminal	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	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Alias	CVaR, expected shortfall, average value-at-risk, tail VaR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Relacionados	5	5	5
Resumen	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).	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.