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Enhancing the COS method with machine learning
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DOI：10.1080/00207160.2025.2574479.png)
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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.
Keyword:
Computational finance
COS method
error control
machine learning
option pricing
C15
C32
C63
G13
期刊
I
IF:
1.3
论文数:
98
被引数:
0
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OAAI
OAAI分享收藏Junike, G. (2024) On the number of terms in the COS method for European option pricing. Numerische Mathematik, 156, 533–564.Junike, G. (2024) 关于COS方法中欧式期权定价项数的探讨。数值数学，156，533–564。.png)
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