方法对比
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| ARIMA（自回归积分滑动平均）模型× | 高频数据与市场微观结构分析× | |
|---|---|---|
| 领域≠ | 计量经济学 | 金融学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 | 2007 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) |
| 类型≠ | Univariate time-series model | Market microstructure / high-frequency econometrics |
| 开创性文献≠ | 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 | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı |
| 相关 | 5 | 5 |
| 摘要≠ | 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). | 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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