Likelihood-Based Estimation of Latent Generalized ARCH Structures
提出一种马尔可夫链蒙特卡洛算法，用于精确估计GARCH模型的似然，并通过模拟和英国26个行业股票收益数据验证其性能，优于现有近似方法。
GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the performance of our proposed algorithm in the context of both artificial examples and an empirical application to 26 UK sectorial stock returns, and compare it to existing approximate solutions.