Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model
针对时变GARCH-M模型计算困难的问题，提出一种马尔可夫链蒙特卡洛算法，能在O(T)步内完成经典或贝叶斯估计，并推导了时变EGARCH(1,1)-M的理论动态性质，应用于三大股票市场。
Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O ( T ) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1, 1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.