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arXiv 2408.01642q-fin.CPq-fin.MFq-fin.PRstat.ML
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用于期权定价的可加过程神经期限结构

Neural Term Structure of Additive Process for Option Pricing

Jimin Lin, Guixin Liu

AI总结:

本文提出用前馈神经网络表示可加过程的期限结构,以解决期权定价中可加模型因时间依赖参数化而难以校准的问题,并通过标普500期权数据验证其性能。

摘要:

可加过程通过放宽莱维过程对时间齐次增量的假设而对其进行了推广,因此覆盖了更大的一族随机过程。期权定价领域的近期研究表明,用可加过程对标的对数价格进行建模具有以下优势:更容易构造风险中性测度、具有显式的期权定价公式和特征函数,并且在拟合隐含波动率曲面时具有更大的灵活性。然而,校准可加模型的挑战源于其依赖时间的参数化方式,为此必须为期限结构指定参数函数。针对这一问题,我们提出了神经期限结构模型,利用前馈神经网络来表示期限结构,从而缓解了设计参数函数的困难,并因此降低了模型误设风险。我们使用标准普尔500指数期权数据进行了数值研究,以评估神经期限结构的性能。

英文摘要:

The additive process generalizes the Lévy process by relaxing its assumption of time-homogeneous increments and hence covers a larger family of stochastic processes. Recent research in option pricing shows that modeling the underlying log price with an additive process has advantages in easier construction of the risk-neural measure, an explicit option pricing formula and characteristic function, and more flexibility to fit the implied volatility surface. Still, the challenge of calibrating an additive model arises from its time-dependent parameterization, for which one has to prescribe parametric functions for the term structure. For this, we propose the neural term structure model to utilize feedforward neural networks to represent the term structure, which alleviates the difficulty of designing parametric functions and thus attenuates the misspecification risk. Numerical studies with S\&P 500 option data are conducted to evaluate the performance of the neural term structure.

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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