Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Realizētā volatilitāte un HAR modelis× | ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis× | |
|---|---|---|
| Nozare≠ | Finanses | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2009 | 2015 |
| Autors≠ | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) | Box & Jenkins (Box-Jenkins methodology) |
| Tips≠ | Time-series regression of realized variance | Univariate time-series model |
| Pirmavots≠ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ | 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 |
| Citi nosaukumi≠ | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. | 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). |
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