An application of an n-dimensional fuzzy smoothing filter in financial modeling

M. Holčapek, T. Tichý
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引用次数: 7

Abstract

Data smoothing is an important step within a data processing procedure that allows one to stress the most important pattern of a function relation between a studied object and given variables. Recently, Holčapek and Tichý (2011) suggested a smoothing filter based on fuzzy transform approach of Perfilieva (2004) and compared it to Nadaraya-Watson estimator. However, within the analysis only one independent variable was assumed. By contrast, many real world problems are multidimensional and hence in this paper we generalize the fuzzy smoothing filter into two dimensions and show its application within a common problem of financial engineering and asset pricing, the option implied volatility surface presentation.
n维模糊平滑滤波器在金融建模中的应用
数据平滑是数据处理过程中的一个重要步骤,它允许人们强调研究对象和给定变量之间函数关系的最重要模式。最近hol apek和Tichý(2011)提出了一种基于Perfilieva(2004)模糊变换方法的平滑滤波器,并将其与Nadaraya-Watson估计器进行了比较。然而,在分析中只假设了一个自变量。相比之下,许多现实世界的问题是多维的,因此在本文中我们将模糊平滑滤波器推广到二维,并展示其在金融工程和资产定价的常见问题——期权隐含波动率面表示中的应用。
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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