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Calibration of the Volatility in Option Pricing Using the Total Variation Regularization
Author(s) -
Zeng Yu-hua,
Shou-Lei Wang,
Yu-Fei Yang
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/510819
Subject(s) - volatility (finance) , econometrics , stochastic volatility , regularization (linguistics) , bivariate analysis , implied volatility , mathematics , local volatility , mathematical optimization , computer science , statistics , artificial intelligence
In market transactions, volatility, which is a very important risk measurement in financial economics, has significantly intimate connection with the future risk of the underlying assets. Identifying the implied volatility is a typical PDE inverse problem. In this paper, based on the total variation regularization strategy, a bivariate total variation regularization model is proposed to estimate the implied volatility. We not only prove the existence of the solution, but also provide the necessary condition of the optimal control problem—Euler-Lagrange equation. The stability and convergence analyses for the proposed approach are also given. Finally, numerical experiments have been carried out to show the effectiveness of the method

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