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arXiv 2410.04487math.NAcs.NAq-fin.CP
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离散概率分布的傅里叶余弦方法

The Fourier Cosine Method for Discrete Probability Distributions

Xiaoyu Shen, Fang Fang, Chengguang Liu

AI总结:

该研究将半解析傅里叶余弦(COS)公式推广至离散概率分布,证明经典谱滤波器收敛速率比此前认知快一阶,数值实验验证了理论,且该方法在特征函数已知时可绕开DFT,适用于计算统计与量化金融场景。

摘要:

我们提供了严格的收敛证明,表明借助谱滤波器,著名的用于连续概率分布傅里叶逆变换的半解析傅里叶余弦(COS)公式可推广至离散概率分布。我们建立了这些滤波器的通用收敛速率,并进一步证明几种经典谱滤波器的收敛速率比吉布斯现象相关文献中此前公认的快一个阶数。我们的数值实验验证了理论收敛结果。此外,我们通过计算统计学与量化金融领域的应用,展示了离散COS方法的计算速度与精度。理论与数值结果凸显了该方法在求解涉及离散分布的问题方面的潜力,尤其是在特征函数已知的情况下,可绕开离散傅里叶变换(DFT)。

英文摘要:

We provide a rigorous convergence proof demonstrating that the well-known semi-analytical Fourier cosine (COS) formula for the inverse Fourier transform of continuous probability distributions can be extended to discrete probability distributions, with the help of spectral filters. We establish general convergence rates for these filters and further show that several classical spectral filters achieve convergence rates one order faster than previously recognized in the literature on the Gibbs phenomenon. Our numerical experiments corroborate the theoretical convergence results. Additionally, we illustrate the computational speed and accuracy of the discrete COS method with applications in computational statistics and quantitative finance. The theoretical and numerical results highlight the method's potential for solving problems involving discrete distributions, particularly when the characteristic function is known, allowing the discrete Fourier transform (DFT) to be bypassed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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