The Chebyshev Method for the Implied Volatility

K. Glau, Paul Herold, D. Madan, C. Pötz
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引用次数: 12

Abstract

The implied volatility is a crucial element of any financial toolbox, since it is used for quoting and the hedging of options as well as for model calibration. In contrast to the Black-Scholes formula its inverse, the implied volatility, is not explicitly available and numerical approximation is required. We propose a bivariate interpolation of the implied volatility surface based on Chebyshev polynomials. This yields a closed-form approximation of the implied volatility, which is easy to implement and to maintain. We prove a subexponential error decay. This allows us to obtain an accuracy close to machine precision with polynomials of a low degree. We compare the performance of the method in terms of runtime and accuracy to the most common reference methods. In contrast to existing interpolation methods, the proposed method is able to compute the implied volatility for all relevant option data. In this context, numerical experiments confirm a considerable increase in efficiency, especially for large data sets.
隐含波动率的Chebyshev方法
隐含波动率是任何金融工具箱的关键元素,因为它用于报价和期权套期保值以及模型校准。与布莱克-斯科尔斯公式相反,它的逆,即隐含波动率,不是明确可用的,需要数值近似。提出了一种基于切比雪夫多项式的隐含波动面二元插值方法。这产生了隐含波动率的封闭近似,易于实现和维护。我们证明了一个次指数误差衰减。这使我们能够用低阶多项式获得接近机器精度的精度。我们将该方法在运行时间和准确性方面的性能与最常见的参考方法进行比较。与现有的插值方法相比,该方法能够计算所有相关期权数据的隐含波动率。在这种情况下,数值实验证实了效率的显著提高,特别是对于大数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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