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Calibration of Option Pricing in Reproducing Kernel Hilbert Space

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Date Issued:
2015
Abstract/Description:
A parameter used in the Black-Scholes equation, volatility, is a measure for variation of the price of a financial instrument over time. Determining volatility is a fundamental issue in the valuation of financial instruments. This gives rise to an inverse problem known as the calibration problem for option pricing. This problem is shown to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of the options. We discuss the existence of the minimizer by using regu- larized reproducing kernel method and show that the regularizer resolves the numerical instability of the calibration problem. Finally, we apply our studied method to data sets of index options by simulation tests and discuss the empirical results obtained.
Title: Calibration of Option Pricing in Reproducing Kernel Hilbert Space.
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Name(s): Ge, Lei, Author
Nashed, M, Committee Chair
Yong, Jiongmin, Committee Member
Qi, Yuanwei, Committee Member
Sun, Qiyu, Committee Member
Caputo, Michael, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2015
Publisher: University of Central Florida
Language(s): English
Abstract/Description: A parameter used in the Black-Scholes equation, volatility, is a measure for variation of the price of a financial instrument over time. Determining volatility is a fundamental issue in the valuation of financial instruments. This gives rise to an inverse problem known as the calibration problem for option pricing. This problem is shown to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of the options. We discuss the existence of the minimizer by using regu- larized reproducing kernel method and show that the regularizer resolves the numerical instability of the calibration problem. Finally, we apply our studied method to data sets of index options by simulation tests and discuss the empirical results obtained.
Identifier: CFE0005617 (IID), ucf:50211 (fedora)
Note(s): 2015-05-01
Ph.D.
Sciences, Mathematics
Doctoral
This record was generated from author submitted information.
Subject(s): RKHS -- Tikhonov regularization -- local volatility -- kernel estimation -- Ridge Regression
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005617
Restrictions on Access: public 2015-05-15
Host Institution: UCF

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