Option pricing with state‐dependent pricing kernel
提出一种结合马尔可夫转换与已实现GARCH的新波动率模型，用于期权定价，推导出具有状态依赖方差风险溢价的定价核和欧式期权解析近似公式，在S&P 500指数期权数据上表现优于现有模型，定价误差降低15%以上。
Abstract We introduce a new volatility model for option pricing that combines Markov switching with the realized generalized autoregressive conditional heteroskedasticity (GARCH) framework. This leads to a novel pricing kernel with a state‐dependent variance risk premium and a pricing formula for European options, which is derived with an analytical approximation method. We apply the Markov‐switching Realized GARCH model to Standard and Poor's 500 index options from 1990 to 2019 and find that investors' aversion to volatility‐specific risk is time‐varying. The proposed framework outperforms competing models and reduces (in‐sample and out‐of‐sample) option‐pricing errors by 15% or more.