Calibration of Option Pricing in Reproducing Kernel Hilbert Space

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.

Author dc:creator

• Ge, Lei

Contributors dc:contributor

• Nashed, M

• Rkhs
• tikhonov regularization
• local volatility
• kernel estimation
• ridge regression
• Mathematics

Language dc:language

English

Identifier

CFE0005617

OAI identifier oai:identifier

oai:stars.library.ucf.edu:etd-1075