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| Mô hình bộ nhớ dài (ARFIMA, FIGARCH)× | Mô hình GARCH (Dự báo Biến động)× | Dữ liệu tần suất cao và phân tích cấu trúc vi mô thị trường× | |
|---|---|---|---|
| Lĩnh vực≠ | Tài chính | Kinh tế lượng | Tài chính |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 1980 | 1986 | 2007 |
| Người khởi xướng≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Tim Bollerslev | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| Loại≠ | Fractionally integrated time series model | Conditional volatility model | Market microstructure / high-frequency econometrics |
| Công trình gốc≠ | 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 ↗ | 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 |
| Tên gọi khác≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | 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 |
| Liên quan≠ | 4 | 5 | 5 |
| Tóm tắt≠ | 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. | 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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