An adaptive model for security prices driven by latent values: parameter estimation and option pricing effects
提出一个股票对数价格向潜在内在价值回归的模型，使用卡尔曼滤波估计参数，发现该方法得到的波动率估计与标准方法差异显著，并展示了不当估计对期权定价和Delta对冲的影响。
We develop a model where the log of stock price is regressive to latent intrinsic value. The model is similar to an Ornstein–Uhlenbeck model but differs in that log price reverts to stochastic intrinsic value. Since intrinsic value is latent, we use a Kalman filter to estimate parameters. The model generates autocorrelated residuals and the Kalman filter yields volatility estimates that are considerably different from those that assume log price differences are independent. We price calls using both the standard estimators and those derived from the Kalman filter to demonstrate the impact of inappropriate estimation techniques. We estimate parameters for selected stocks and test delta hedging performance versus alternative models.