Fast Filtering with Large Option Panels: Implications for Asset Pricing
提出一种粒子马尔可夫链蒙特卡洛框架，结合新型滤波方法，用于估计指数期权定价模型，发现方差风险溢价、方差均值回归和高阶矩的估计值因期权样本构成而异，其中期限维度对参数推断和期权拟合影响最大。
Abstract The cross section of options holds great promise for identifying return distributions and risk premia, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle Markov Chain Monte Carlo framework with a novel filtering approach and illustrate our method by estimating index option pricing models. Estimates of variance risk premiums, variance mean reversion, and higher moments differ from the literature. We show that these differences are due to the composition of the option sample. Restricting the option sample’s maturity dimension has the strongest impact on parameter inference and option fit in these models.