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| GARCH-Modell (Volatilitätsvorhersage)× | Hochfrequenzdaten und Analyse der Marktmikrostruktur× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Finanzwirtschaft |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1986 | 2007 |
| Urheber≠ | Tim Bollerslev | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| Typ≠ | Conditional volatility model | Market microstructure / high-frequency econometrics |
| Wegweisende Quelle≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 |
| Aliasnamen | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014). |
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