Index Option Pricing via Nonparametric Regression

Ka Po Kung
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Ka Po Kung: National University of Singapore, Singapore

Econometric Research in Finance, 2022, vol. 7, issue 1, 125-142

Abstract: 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.

Keywords: Black-Scholes Parametric Model; Black-Scholes Nonparametric Models; Index Options; Volatility; Kernels (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sgh:erfinj:v:7:y:2022:i:1:p:125-142

DOI: 10.2478/erfin-2022-0004

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