Journal of Risk Model Validation

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A verification model to capture option risk and hedging based on a modified underlying beta

Chuan-He Shen and Yang Liu

  • This paper employed the modified underlying beta to propose a verification model that can capture option risks and hedge.
  • Two methods, such as kurtosis-skewness, and curvature-high order moments-error terms, which deal with volatility clusters and nonlinear characteristics, were used to estimate the underlying beta.
  • After empirical research, we found that the hedging effect of the underlying beta model improved by curvature-high order moment-error terms is superior to the model of the underlying beta adjusted by kurtosis-skewness, and they are all better than the original underlying beta model to some extent.

The mining and hedging of option volatility information are the core issues of stock option markets. This paper analyzes the relationship between option risk and expected return from the perspective of the underlying beta, and estimates the degree of correlation. As the assumptions of the capital asset pricing model and Black–Scholes model are not consistent with the actual situation in the financial market, we use applied statistical models to introduce kurtosis and skewness, and to introduce curvature and high-order-moment error terms to optimize the underlying beta model. We then develop a verification model for mining option risk and hedging by employing the modified underlying beta. We verify the hedging performance of the above model by choosing different market samples, such as the China, Hong Kong and US financial markets. The results show that the hedging performance of the optimized underlying beta model in the US market is most satisfactory, followed by the Hong Kong market and then the Chinese mainland market. Meanwhile, the hedging effect of the underlying beta model improved by curvature and high-order-moment error terms is superior to that of the model of the underlying beta adjusted by the kurtosis and skewness.

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