Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de volatilidad estocástica (Heston)× | Modelos de memoria larga (ARFIMA, FIGARCH)× | |
|---|---|---|
| Campo | Finanzas | Finanzas |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1993 | 1980 |
| Autor original≠ | Steven L. Heston | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| Tipo≠ | Continuous-time stochastic volatility model | Fractionally integrated time series model |
| Fuente seminal≠ | Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6(2), 327-343. DOI ↗ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ |
| Alias≠ | Heston model, SV model, continuous-time stochastic volatility, Stokastik Volatilite Modeli (Heston, SV) | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | The stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option price; it is the continuous-time counterpart of GARCH. | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. |
| ScholarGateConjunto de datos ↗ |
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