방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Longstaff-Schwartz 방법× | 무위험 중립 가치 평가× | |
|---|---|---|
| 분야 | 금융공학 | 금융공학 |
| 계열≠ | Machine learning | Regression model |
| 기원 연도≠ | 2001 | 1979 |
| 창시자≠ | Francis A. Longstaff and Eduardo S. Schwartz | John Harrison and David Kreps |
| 유형≠ | Valuation Algorithm | Fundamental Principle |
| 원전≠ | 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 ↗ | Harrison, J. M., & Kreps, D. M. (1979). Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory, 20(3), 381-408. DOI ↗ |
| 별칭≠ | LSM, Least-Squares MC, Optimal Stopping | Risk-Neutral Measure, Q-Measure |
| 관련 | 4 | 4 |
| 요약≠ | 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. | Risk-neutral valuation (1979) is the fundamental principle that derivative prices equal the expected payoff discounted at the risk-free rate, computed under a risk-neutral probability measure (Q-measure). This principle, formalized by Harrison and Kreps, eliminates the need to estimate risk premia and is the foundation of modern derivatives pricing. |
| ScholarGate데이터셋 ↗ |
|
|