方法对比
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| 非线性GARCH模型× | 自回归条件异方差 (ARCH) 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≠ | 1991-1993 | 1982 |
| 提出者≠ | Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH | Robert F. Engle |
| 类型≠ | Volatility model | Conditional volatility model |
| 开创性文献≠ | 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. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| 别名 | NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| 相关 | 6 | 6 |
| 摘要≠ | 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. |
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