Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| HAR-RV model of realized volatility× | Markova režīmu pārslēgšanās modelis finanšu sērijām× | |
|---|---|---|
| Nozare | Finanses | Finanses |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2009 | 1989 |
| Autors≠ | Fulvio Corsi | James D. Hamilton |
| Tips≠ | Linear time-series regression for volatility | Markov regime-switching 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 ↗ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ |
| Citi nosaukumi≠ | HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility) | Markov switching model, Hamilton regime-switching model, MS-AR, hidden Markov regime model |
| Saistītās≠ | 5 | 1 |
| Kopsavilkums≠ | 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. | The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time. |
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