Сравнение методов

	Бэктестинг Value-at-Risk (VaR)	Модель GARCH (прогнозирование волатильности)	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Финансы	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1998	1986	2019
Автор метода	Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Тип	Statistical hypothesis tests on VaR violation sequences	Conditional volatility model	Linear regression
Основополагающий источник	Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	VaR backtest, Kupiec test, Christoffersen test, Dynamic Quantile test	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	3	5	5
Сводка	VaR backtesting is a family of statistical tests that validate a risk model by comparing its Value-at-Risk forecasts against realised losses. It builds on Kupiec's (1995) unconditional coverage test, Christoffersen's (1998) conditional coverage test, and the Engle-Manganelli Dynamic Quantile (DQ) test.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).