方法对比
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| Longstaff-Schwartz 方法× | SABR模型× | |
|---|---|---|
| 领域 | 量化金融 | 量化金融 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 2001 | 2002 |
| 提出者≠ | Francis A. Longstaff and Eduardo S. Schwartz | Patrick S. Hagan |
| 类型≠ | Valuation Algorithm | Interest Rate Model |
| 开创性文献≠ | Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least-squares approach. Review of Financial Studies, 14(1), 113-147. DOI ↗ | Hagan, P. S., Kumar, D., Lesniewski, A. S., & Woodward, D. E. (2002). Managing smile risk. Wilmott Magazine, 1, 84-108. link ↗ |
| 别名≠ | LSM, Least-Squares MC, Optimal Stopping | Stochastic Volatility Model |
| 相关 | 4 | 4 |
| 摘要≠ | The Longstaff-Schwartz method (2001) is a Monte Carlo algorithm for pricing American options and Bermudan swaptions by approximating the optimal exercise boundary via least-squares regression. It has become the industry standard for pricing path-dependent derivatives where analytical solutions do not exist. | The SABR (Stochastic Alpha-Beta-Rho) model is a stochastic volatility framework introduced by Hagan et al. in 2002 for valuing interest rate derivatives. It captures the smile effect in implied volatility through correlated Brownian motions and has become industry standard for swaption and caplet pricing. |
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