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arXiv 1702.05036q-fin.MF
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具有随机界的不确定波动率模型

Uncertain Volatility Models with Stochastic Bounds

Jean-Pierre Fouque, Ning Ning

摘要:

本文提出了具有随机界的不确定波动率模型。与常规的不确定波动率模型一样,我们只知道真正的模型存在于一系列渐进可测和有界的过程中,但不使用两个确定性边界,不确定波动率在由其固有的随机波动率过程产生的两个随机边界之间波动。这带来了更好的准确性,并与观察到的波动路径一致,例如VIX作为代理。我们应用正则摄动分析的最坏情况下的价格,并推导出一阶近似的制度缓慢变化的随机边界。原来的问题,其中涉及解决一个完全非线性偏微分方程在二维的最坏情况下的价格,减少到解决一个非线性偏微分方程在一维和线性偏微分方程的源,这给了一个巨大的计算优势。数值实验表明,这种近似过程执行得很好,即使在制度的适度缓慢变化的随机边界。

英文摘要:

In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between two stochastic bounds generated by its inherent stochastic volatility process. This brings better accuracy and is consistent with the observed volatility path such as for the VIX as a proxy for instance. We apply the regular perturbation analysis upon the worst case scenario price, and derive the first order approximation in the regime of slowly varying stochastic bounds. The original problem which involves solving a fully nonlinear PDE in dimension two for the worst case scenario price, is reduced to solving a nonlinear PDE in dimension one and a linear PDE with source, which gives a tremendous computational advantage. Numerical experiments show that this approximation procedure performs very well, even in the regime of moderately slow varying stochastic bounds.

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