Previous options studies typically assume that the dynamics of the underlying asset price follow a geometric Brownian motion GBM when pricing options on stocks, stock indices, currencies or futures.

However, there is mounting empirical evidence that the volatility of asset price or return is far from constant. This article, in contrast to studies that use parametric approach for option pricing, employs nonparametric kernel regression to deal with changing volatility and, accordingly, prices options on stock index.

Specifically, we first estimate nonparametrically the volatility of asset return in the GBM based on the Nadaraya—Watson N—W kernel estimator. Then, based on the N—W estimates for the volatility, we use Monte Carlo simulation to compute option prices under different settings. Finally, we compare the index option prices under our nonparametric model with those under the Black—Scholes model and the Stein—Stein model.

A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns

Article Purchase 24 hours access for EUR 35, Issue Purchase 30 days access for EUR , Journal Applied Economics Volume 48, - Issue Submit an article Journal homepage. Kung Department of International Business, Ming Chuan University, Taipei, Taiwan Correspondence fnjames mail.

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