Sequential Bayesian Analysis of Time-Changed Infinite Activity Derivatives Pricing Models
提出一种结合无迹卡尔曼滤波的序贯蒙特卡洛方法，用于时间变换无限活动衍生品定价模型，解决了传统粒子滤波的粒子贫化问题，并利用期权数据大幅提升状态滤波精度。
This article investigates time-changed infinite activity derivatives pricing models from the sequential Bayesian perspective. It proposes a sequential Monte Carlo method with the proposal density generated by the unscented Kalman filter. This approach overcomes to a large extent the particle impoverishment problem inherent to the conventional particle filter. Simulation study and real applications indicate that (1) using the underlying alone cannot capture the dynamics of states, and by including options, the precision of state filtering is dramatically improved; (2) the proposed method performs better and is more robust than the conventional one; and (3) joint identification of the diffusion, stochastic volatility, and jumps can be achieved using both the underlying data and the options data.