Linganisha mbinu

	Nadharia ya Thamani Iliyokithiri (EVT)	Exponential GARCH (EGARCH)	Nadharia ya Hisa Zinazotambulika na Muundo wa HAR
Nyanja	Fedha	Ekonometriki	Fedha
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2001	1991	2009
Mwanzilishi	Coles (textbook treatment); McNeil, Frey & Embrechts	Nelson	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Aina	Tail / extreme-event model	Conditional volatility model (asymmetric GARCH variant)	Time-series regression of realized variance
Chanzo asilia	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Majina mbadala	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Zinazohusiana	5	4	5
Muhtasari	Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.	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.	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.