Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| HAR-RV model of realized volatility× | Viļņu analīze finanšu laika rindām× | |
|---|---|---|
| Nozare | Finanses | Finanses |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2009 | 2001 |
| Autors≠ | Fulvio Corsi | Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares |
| Tips≠ | Linear time-series regression for volatility | Time-frequency decomposition |
| Pirmavots≠ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196. DOI ↗ | Gençay, R., Selçuk, F. & Whitcher, B. (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press. DOI ↗ |
| Citi nosaukumi | HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility) | wavelet coherence, continuous wavelet transform, time-frequency analysis, Dalgacık (Wavelet) Finansal Analiz |
| 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. | Wavelet financial analysis decomposes a financial time series into different frequency bands (time scales) so short- and long-term relationships can be studied at the same time. Drawing on the treatments of Gençay, Selçuk and Whitcher (2001) and Aguiar-Conraria and Soares (2014), wavelet coherence then visualises how the relationship between two series shifts across both time and frequency. |
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