Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел ARIMA (Autoregressive Integrated Moving Average)× | Модел GARCH (Прогнозиране на волатилността)× | Анализ на високочестотни данни и пазарна микроструктура× | |
|---|---|---|---|
| Област≠ | Иконометрия | Иконометрия | Финанси |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2015 | 1986 | 2007 |
| Създател≠ | Box & Jenkins (Box-Jenkins methodology) | Tim Bollerslev | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| Тип≠ | Univariate time-series model | Conditional volatility model | Market microstructure / high-frequency econometrics |
| Основополагащ източник≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | 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 |
| Други названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | 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 |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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). |
| ScholarGateНабор от данни ↗ |
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