Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa ARCH (Autoregressive Conditional Heteroskedasticity)× | Modeli wa GARCH (Utabiri wa Msukosuko)× | Regression ya Kiasi (Quantile Regression)× | |
|---|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model | Regression model |
| Mwaka wa asili≠ | 1982 | 1986 | 1978 |
| Mwanzilishi≠ | Robert F. Engle | Tim Bollerslev | Koenker & Bassett |
| Aina≠ | Conditional volatility model | Conditional volatility model | Conditional quantile regression |
| Chanzo asilia≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Majina mbadala≠ | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Zinazohusiana≠ | 6 | 5 | 5 |
| Muhtasari≠ | 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. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. | 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. |
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