Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Высокочастотные данные и анализ микроструктуры рынка× | Модель HAR-RV реализованной волатильности× | |
|---|---|---|
| Область | Финансы | Финансы |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2007 | 2009 |
| Автор метода≠ | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) | Fulvio Corsi |
| Тип≠ | Market microstructure / high-frequency econometrics | Linear time-series regression for volatility |
| Основополагающий источник≠ | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196. DOI ↗ |
| Другие названия | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı | HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility) |
| Связанные | 5 | 5 |
| Сводка≠ | 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). | The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investment horizons react to volatility, and it naturally captures the long-memory behaviour of volatility. |
| ScholarGateНабор данных ↗ |
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