2006/08/01 by Cheng-Der Fuh
Economics, Econometrics and Finance · Mathematics · #Estimation theory #Estimator #Financial Risk and Volatility Modeling #Likelihood function #Marginal likelihood #Markov chain #Maximum likelihood #Maximum likelihood sequence estimation #Sequence (biology) #State space #Statistical Methods and Inference #Stochastic processes and financial applications #Stochastic volatility #math.ST #stat.TH
paper · pdf · doi:10.1214/009053606000000614
published as Annals of Statistics 2006, Vol. 34, No. 4, 2026-2068 · With the comments by Jens Ledet Jensen and reply to the comments. Published at http://dx.doi.org/10.1214/009053606000000614; http://dx.doi.org/10.1214/09-AOS748A; http://dx.doi.org/10.1214/09-AOS748B in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2006/08/01 · arxiv created 2010/11/12 · arxiv updated 2010/11/15 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Motivated by studying asymptotic properties of the maximum likelihood estimator (MLE) in stochastic volatility (SV) models, in this paper we investigate likelihood estimation in state space models. We first prove, under some regularity conditions, there is a consistent sequence of roots of the likelihood equation that is asymptotically normal with the inverse of the Fisher information as its variance. With an extra assumption that the likelihood equation has a unique root for each n, then there is a consistent sequence of estimators of the unknown parameters. If, in addition, the supremum of the log likelihood function is integrable, the MLE exists and is strongly consistent. Edgeworth expansion of the approximate solution of likelihood equation is also established. Several examples, including Markov switching models, ARMA models, (G)ARCH models and stochastic volatility (SV) models, are given for illustration.