Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель стохастичної волатильності (Гестон)× | Моделі довгої пам'яті (ARFIMA, FIGARCH)× | |
|---|---|---|
| Галузь | Фінанси | Фінанси |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1993 | 1980 |
| Автор методу≠ | Steven L. Heston | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| Тип≠ | Continuous-time stochastic volatility model | Fractionally integrated time series model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | Heston model, SV model, continuous-time stochastic volatility, Stokastik Volatilite Modeli (Heston, SV) | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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