Сравнение методов

	Нелинейная модель GARCH	Модель ARCH (авторегрессионная условная гетероскедастичность)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1991-1993	1982
Автор метода	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Robert F. Engle
Тип	Volatility model	Conditional volatility model
Основополагающий источник	Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Другие названия	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Связанные	6	6
Сводка	The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.