השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל ARCH (Autoregressive Conditional Heteroskedasticity)× | מודל EGARCH (Exponential GARCH)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1982 | 1991 |
| הוגה השיטה≠ | Robert F. Engle | Daniel B. Nelson |
| סוג≠ | Conditional volatility model | Volatility / conditional variance model |
| מקור מכונן≠ | Engle, 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 ↗ |
| כינויים | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| קשורות | 6 | 6 |
| תקציר≠ | 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 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. |
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