Likelihood-based estimation of latent generalized arch structures

• 成果类型: Article
• 署名作者: Fiorentini, G; Sentana, E; Shephard, N
• 署名单位: University of Florence; University of Oxford
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2004.00541.x
• 发表日期: 2004
• 页码: 1481-1517
• 关键词: time-series models; CONDITIONAL HETEROSCEDASTICITY; BAYES INFERENCE; PRICE LIMITS; volatility; variance; futures; distributions; arbitrage; DYNAMICS

摘要

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.