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Просматривайте выбранные методы рядом; строки с различиями подсвечены.

Модель ARCH (авторегрессионная условная гетероскедастичность)×Модель EGARCH (Экспоненциальная GARCH)×Квантильная регрессия×
ОбластьЭконометрикаЭконометрикаЭконометрика
СемействоRegression modelRegression modelRegression model
Год появления198219911978
Автор методаRobert F. EngleDaniel B. NelsonKoenker & Bassett
ТипConditional volatility modelVolatility / conditional variance modelConditional quantile regression
Основополагающий источникEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Другие названияARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance modelExponential GARCH, EGARCH, Nelson EGARCH, log-GARCHconditional quantile regression, regression quantiles, Kantil Regresyon
Связанные665
СводкаThe 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.The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
ScholarGateНабор данных
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ScholarGateСравнение методов: ARCH model · EGARCH model · Quantile Regression. Получено 2026-06-18 из https://scholargate.app/ru/compare