Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель Фур'є-GARCH× | Модель АРХ (Авторегресивна умовна гетероскедастичність)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2000–2012 | 1982 |
| Автор методу≠ | Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework | Robert F. Engle |
| Тип≠ | Volatility model | Conditional volatility model |
| Основоположне джерело≠ | Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Інші назви | Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | The Fourier GARCH model embeds trigonometric Fourier terms into a standard GARCH framework to capture smooth, gradual shifts in the conditional variance process without requiring knowledge of exact structural break dates. By approximating unknown break patterns with sinusoidal functions, it jointly models volatility clustering and time-varying unconditional variance. | 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. |
| ScholarGateНабір даних ↗ |
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