方法对比
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| 傅里叶自回归条件异方差模型 (Fourier ARCH Model)× | 傅里叶 GARCH 模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010s | 2000–2012 |
| 提出者≠ | Extends Engle (1982) ARCH framework with Fourier terms following Enders & Lee (2012) | Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework |
| 类型≠ | Volatility model with smooth structural change | Volatility model |
| 开创性文献≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347. DOI ↗ |
| 别名 | Fourier-ARCH, F-ARCH, ARCH with Fourier terms, Fourier smooth transition ARCH | Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH |
| 相关≠ | 6 | 5 |
| 摘要≠ | The Fourier ARCH model extends the classical ARCH framework by incorporating trigonometric (Fourier) terms into the conditional variance equation. This allows the model to capture smooth, gradual shifts in volatility dynamics over time without assuming abrupt structural breaks, making it well-suited for long financial or macroeconomic time series subject to slowly evolving regime changes. | 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. |
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