विधियों की तुलना करें

	चरम मान सिद्धांत (EVT)	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	वास्तविक अस्थिरता और HAR मॉडल
क्षेत्र	वित्त	अर्थमिति	वित्त
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	2001	2015	2009
प्रवर्तक	Coles (textbook treatment); McNeil, Frey & Embrechts	Box & Jenkins (Box-Jenkins methodology)	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
प्रकार	Tail / extreme-event model	Univariate time-series model	Time-series regression of realized variance
मौलिक स्रोत	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598	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.
उपनाम	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
संबंधित	5	5	5
सारांश	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.	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.