Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Grieķi (Greeks) izmantojot automātisko diferenciāciju× | Novērtēšana pret risku neitrālā pasaulē× | |
|---|---|---|
| Nozare | Kvantitatīvās finanses | Kvantitatīvās finanses |
| Saime≠ | Machine learning | Regression model |
| Izcelsmes gads≠ | 2008 | 1979 |
| Autors≠ | Mike Giles, Iman Homescu | John Harrison and David Kreps |
| Tips≠ | Sensitivity Analysis | Fundamental Principle |
| Pirmavots≠ | Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18. link ↗ | Harrison, J. M., & Kreps, D. M. (1979). Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory, 20(3), 381-408. DOI ↗ |
| Citi nosaukumi≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Risk-Neutral Measure, Q-Measure |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | Automatic differentiation (AD) is a computational technique for computing derivatives (Greeks) by differentiating the computer code that computes the option price. AD avoids manual derivation of formulas and finite-difference approximations, yielding exact sensitivities with machine precision. It has become essential for real-time risk management in modern trading systems. | 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. |
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