Option pricing with dynamic conditional skewness
提出了一个离散时间仿射期权定价模型，显式纳入条件偏度的动态变化，利用高频历史收益构建的已实现测度更新偏度和方差，通过傅里叶反演得到闭式期权估值公式，实证显示在标普500指数期权上定价精度提升12.25%。
Abstract In this paper, we develop a discrete‐time affine option‐pricing model that explicitly incorporates the dynamics of conditional skewness. The new proposed model features different dynamics for conditional skewness and variance. To stress the difference in information, we use alternative realized measures constructed from high‐frequency historical returns to update skewness and variance dynamics. By Fourier inversion, we derive closed‐form option valuation formulas. Empirically, the flexibility that the model offers for conditional skewness as well as high‐frequency information from the underlying asset contribute to superior performance upon benchmark models using S&P 500 index options. Overall, the joint modeling of dynamic conditional skewness and realized measures leads to an out‐of‐sample gain of 12.25% in pricing accuracy. The improvements are more pronounced for deep in‐the‐money calls, options with shorter maturities, and during highly volatile periods.