Сравнение на методи

	Тестване на устойчивостта на стойността при риск (VaR)	Модел GARCH (Прогнозиране на волатилността)	Модел HAR-RV на реализираната волатилност
Област	Финанси	Иконометрия	Финанси
Семейство	Regression model	Regression model	Regression model
Година на възникване	1998	1986	2009
Създател	Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)	Tim Bollerslev	Fulvio Corsi
Тип	Statistical hypothesis tests on VaR violation sequences	Conditional volatility model	Linear time-series regression for volatility
Основополагащ източник	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.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.
Други названия	VaR backtest, Kupiec test, Christoffersen test, Dynamic Quantile test	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)
Свързани	3	5	5
Резюме	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.	The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investment horizons react to volatility, and it naturally captures the long-memory behaviour of volatility.