Persistent and transient variance components in option pricing models with variance-dependent Kernel
在连续时间双因子随机波动率模型中引入依赖方差的定价核，对比其与离散时间GARCH模型在捕捉期限结构和微笑/偏斜模式上的表现，发现双因子SV模型在样本外拟合更优且能减少执行价格偏差。
This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data. • I examine a variance-dependent kernel in a two-factor stochastic volatility model. • The model performance in capturing term structure and smile/smirk is investigated. • The study finds negative and significant risk premiums for both volatility factors. • In-sample, CGARCH exhibits a slightly better fit than two-factor SV model. • Out-of-sample, the two-factor SV model has better fit to data.