Implied Volatility Surface Estimation Using Transductive Gaussian Fields Regression

Abstract:

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Implied volatility estimation is one of the fundamental tasks for asset pricing and risk management. In this paper, we investigate the applicability of semi-supervised regression techniques to estimate an implied volatility surface from the real market option data. Specifically, we employ a transductive Gaussian field regression method since it is able to predict a distribution of the implied volatilities for unlabelled data using only partially labeled data. We've conducted simulation on S&P 500 index data before and after the global financial crisis with discussions of the observed empirical properties of the method.

Info:

Periodical:

Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng

Pages:

1781-1786

DOI:

10.4028/www.scientific.net/KEM.467-469.1781

Citation:

H. J. Park et al., "Implied Volatility Surface Estimation Using Transductive Gaussian Fields Regression", Key Engineering Materials, Vols. 467-469, pp. 1781-1786, 2011

Online since:

February 2011

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Price:

$35.00

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