Filtering for partially observed diffusion and its applications

Autor:	Isao Shoji
Rok vydání:	2013
Předmět:	Statistics and Probability Mathematical optimization Stochastic volatility Consistency (statistics) Conditional moments Estimation theory Analytic element method Kalman filter Diffusion (business) Condensed Matter Physics Algorithm Mathematics
Zdroj:	Physica A: Statistical Mechanics and its Applications. 392:4966-4976
ISSN:	0378-4371
Popis:	This paper provides an analytic method of filtering for partially observed diffusions, which can be also used for parameter estimation with the quasi-maximum likelihood method. The filtering is shown to have consistency in a weak sense. In addition, using the stochastic volatility models, a comparative simulation study is carried out to see how well the proposed method numerically works. The performance of the proposed method is basically better than that of the extended Kalman filtering.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::daf34e40164c1c3036a0cae1d7f776ed https://doi.org/10.1016/j.physa.2013.06.005 Zobrazit plný text záznamu Full Text from ScienceDirect