Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo GARCH (Previsão de Volatilidade)× | Modelo HAR-RV de Volatilidade Realizada× | |
|---|---|---|
| Área≠ | Econometria | Finanças |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1986 | 2009 |
| Autor original≠ | Tim Bollerslev | Fulvio Corsi |
| Tipo≠ | Conditional volatility model | Linear time-series regression for volatility |
| Fonte seminal≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196. DOI ↗ |
| Outros nomes | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility) |
| Relacionados | 5 | 5 |
| Resumo≠ | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. | 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. |
| ScholarGateConjunto de dados ↗ |
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