A Stochastic Volatility Model With Realized Measures for Option Pricing
针对已实现波动率测度存在测量误差的问题，提出一类新的离散时间随机波动率模型，包含两个测量方程分别将观测收益率和已实现测度与潜在条件方差联系起来，并开发了半解析期权定价框架。
Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.