Index Option Pricing via Nonparametric Regression

Investors typically use the Black-Scholes (B-S) parametric model to value financial options. However, there is extensive empirical evidence that the B-S model, assuming constant volatility of stock returns, is far from adequate to price options. This paper, using nonparametric regression, incorpora...

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Bibliographic Details
Main Author: Ka Po Kung
Format: Article
Language:English
Published: SGH Warsaw School of Economics, Collegium of Economic Analysis 2022-12-01
Series:Econometric Research in Finance
Subjects:
Online Access:https://www.erfin.org/journal/index.php/erfin/article/view/168
Description
Summary:Investors typically use the Black-Scholes (B-S) parametric model to value financial options. However, there is extensive empirical evidence that the B-S model, assuming constant volatility of stock returns, is far from adequate to price options. This paper, using nonparametric regression, incorporates a volatility-adjusting mechanism into the B-S model and prices options on the S&P 500 Index. Specifically, the upgraded B-S model, referred to as the B-S nonparametric model, is equipped with such a mechanism whose function is to assign larger volatilities for larger log returns and smaller volatilities for smaller log returns to characterize volatility clustering, a phenomenon such that large/small log returns tend to be followed by large/small log returns. Using the B-S nonparametric models as a yardstick, our simulation results show that, across the board, the B-S parametric model considerably overprices both call and put options.
ISSN:2451-1935
2451-2370