Efficient Estimation of Conditional Asset-Pricing Models
提出一种半参数有效估计方法，用于多元GARCH-in-mean模型，假设扰动项为椭圆对称分布但形式未知，避免维数灾难，适用于条件资产定价模型的估计与检验，并基于股票价格数据进行了实证和蒙特卡洛模拟。
AbstractA semiparametric efficient estimation procedure is developed for the parameters of multivariate generalized autoregressive conditional heteroscedasticity-in-mean models when the disturbances have a conditional distribution assumed to be elliptically symmetric but otherwise unrestricted. Under high-level assumptions, the resulting estimator achieves the asymptotic semiparametric efficiency bound. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that would otherwise arise in estimating the unknown error distribution. This framework is suitable for the estimation and testing of conditional asset-pricing models, such as the conditional capital asset-pricing model. We apply our procedure in an empirical study of stock prices, with Monte Carlo simulation results also reported.KEY WORDS: Capital asset-pricing modelMultivariate autoregressive conditional heteroscedasticitySemiparametric efficiency