A Gaussian semi-parametric implied volatility model. Issue 3 (September 2017)

• Record Type: Journal Article
• Authors: Wu, Xiaoyan; Zhuang, Ying; Chen, Fei; Wang, Meiqing
• Is Part Of: Journal of algorithms & computational technology. Volume 11:Issue 3(2017)
• Page Start: 246
• Page End: 260
• Publication Date: 2017-09
• Subjects: Implied volatility -- semi-parametric model -- exponential parameter -- Gaussian -- Arbitrage-free; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301817709602
• Languages: English
• ISSNs: 1748-3018

Abstract

Modeling the implied volatility has received extensive attention, as the implied volatility is an important parameter in option pricing. Usually the implied volatility can be approximated by fitting a polynomial about the strike and the maturity or by stochastic methods. In this article, a Gaussian semi-parametric model is proposed based on the quadratic polynomial semi-parametric model suggested by Borovkova. In the new model, the Gaussian function is used to construct a smooth term substituting the quadratic term in the polynomial model, and the arbitrage-free constraints are used to calibrate the model. The empirical tests show that the Gaussian semi-parametric model has a better performance in fitting and forecasting.