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Enhancing the COS method with machine learning
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AI
DOI：10.1080/00207160.2025.2574479.png)
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Abstract
En 中文
The Fourier-cosine (COS) method is a classic tool for pricing European options. The method is generally applicable to models where the characteristic function of log-returns is known. A major advantage of the COS method over competing Fourier pricing methods is an effective error control. However, implementing formulas for the tuning parameters of the COS method requires the calculation of higher-order moments of the log-returns. The scope of this paper is twofold: (i) We illustrate how to obtain fully explicit formulas for the high-order moments of the log-returns under affine stochastic volatility models, which is crucial for the efficient implementation of the COS method. (ii) Whenever it is not possible to obtain explicit formulas for high order moments, e.g. for the 3/2 stochastic volatility model, we propose to learn high order moments using machine learning techniques. As a result, we obtain very fast algorithms with almost complete error control.
Keywords:
Computational finance
COS method
error control
machine learning
option pricing
C15
C32
C63
G13
Journal
I
IF:
1.3
Papers:
98
Citations:
0
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