Linganisha mbinu

	Thibitisho la Thamani ya Hatari (Matarajio ya Upungufu)	Mfumo wa ARIMA (Autoregressive Integrated Moving Average)	Regression ya Kiasi (Quantile Regression)
Nyanja	Fedha	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2000	2015	1978
Mwanzilishi	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Box & Jenkins (Box-Jenkins methodology)	Koenker & Bassett
Aina	Coherent tail-risk measure	Univariate time-series model	Conditional quantile regression
Chanzo asilia	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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Majina mbadala	CVaR, expected shortfall, average value-at-risk, tail VaR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Zinazohusiana	5	5	5
Muhtasari	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).	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.