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| Байесов модел GARCH× | Модел ARCH (Авторегресивен условен хетероскедастичност)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1989–2000 | 1982 |
| Създател≠ | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) | Robert F. Engle |
| Тип≠ | Bayesian volatility model | Conditional volatility model |
| Основополагащ източник≠ | Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Други названия | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Свързани≠ | 4 | 6 |
| Резюме≠ | The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics. | 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. |
| ScholarGateНабор от данни ↗ |
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