Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model
提出一个半参数条件协方差估计量，结合参数估计与非参数修正，证明其渐近正态性，并给出模型设定检验，通过模拟和实际数据比较其预测风险表现。
We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct specification of PCC models, and study its asymptotic properties. We evaluate the finite sample performance of our test and SCC estimator and compare the latter with that of the PCC estimator, purely nonparametric estimator, and Hafner, Dijk, and Franses's (2006) estimator in terms of mean squared error and Value-at-Risk losses via simulations and real data analyses.