Abstract
In this paper, we take the advantage of high frequency data to develop option pricing model and select the Realized GARCH model to describe the volatility of assets, use NIG distribution to describe the distribution of underlying assets, and also build the Realized-GARCH-NIG model to price the option. Finally, we obtain the dynamic option pricing model based on the Realized-GARCH-NIG approach. To verify the effect of the dynamic option pricing model based on the Realized-GARCH- NIG approach, this paper provides the empirical analysis between the dynamic option pricing model based on the Realized-GARCH-NIG approach and the B-S option pricing model. The results show that the option value obtained from the dynamic option pricing model based on the Realized-GARCH-NIG approach is more accurate and effective than the B-S option pricing model.
Highlights
Option is a derivative financial tool, which has become an important risk management tool for investors
In this paper we use the NASDAQ 100 Index Option data as research example, give an empirical analysis between the dynamic option pricing model based on the Realized-GARCH-normal inverse gaussian (NIG) approach and the B-S option pricing model
In previous option pricing researches, low frequency data and normal distribution are often used to estimate the dynamic process of assets, those inaccurate estimation methods have a larger influence on option value estimation
Summary
Option is a derivative financial tool, which has become an important risk management tool for investors. Merton and Robert [2] considered the jump process in the market price volatility, and proposed the option pricing model based on market price jump-diffusion process. John, and Alan White [3] built random price volatility model by modeling the volatility with random process method, and proposed the option pricing model with random volatility diffusion. Duan [4] built an option pricing model by estimating the time-varying characteristics of asset volatility with GARCH model. William and Thomas [5] considered the mixed distribution assumption in the price dynamics process, presented the option pricing model that assets return follows the mixed normal distribution, and conducted empirical analysis to obtain better results. Duan [6] considered the characteristics of fat tail, sharp peak and skewness distribution, and built option pricing model based on GARCH-GED. Chorro [7] selected more general generalized hyperbolic distribution to de-
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