Linganisha mbinu

	Upimaji wa Thamani-kwenye-Hatari (VaR) baada ya Kipindi	Mfumo wa HAR-RV wa Matumizi Halisi ya Kutikisika	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Fedha	Fedha	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1998	2009	2019
Mwanzilishi	Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)	Fulvio Corsi	Wooldridge (textbook treatment); classical least squares
Aina	Statistical hypothesis tests on VaR violation sequences	Linear time-series regression for volatility	Linear regression
Chanzo asilia	Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadala	VaR backtest, Kupiec test, Christoffersen test, Dynamic Quantile test	HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	3	5	5
Muhtasari	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 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.	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).