Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Моделі довгої пам'яті (ARFIMA, FIGARCH)× | Аналіз ринкової мікроструктури на основі високочастотних даних× | |
|---|---|---|
| Галузь | Фінанси | Фінанси |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1980 | 2007 |
| Автор методу≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| Тип≠ | Fractionally integrated time series model | Market microstructure / high-frequency econometrics |
| Основоположне джерело≠ | 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 |
| Інші назви≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | 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). |
| ScholarGateНабір даних ↗ |
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