Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèles à mémoire longue (ARFIMA, FIGARCH)× | Données à haute fréquence et analyse de la microstructure de marché× | |
|---|---|---|
| Domaine | Finance | Finance |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1980 | 2007 |
| Auteur d'origine≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| Type≠ | Fractionally integrated time series model | Market microstructure / high-frequency econometrics |
| Source fondatrice≠ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 |
| Alias≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. | 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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