Estimation, filtering and smoothing in the stochastic conditional duration model: an estimating function approach. Issue 1 (12th January 2016)

• Record Type: Journal Article
• Authors: Thekke, Ramanathan; Mishra, Anuj; Abraham, Bovas
• Is Part Of: Stat. Volume 5:Issue 1(2016)
• Page Start: 11
• Page End: 21
• Publication Date: 2016-01-12
• Subjects: estimating function -- filtering and smoothing -- quasi‐maximum likelihood -- stochastic conditional duration model; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.101
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Stochastic conditional duration models are widely used in the financial econometrics literature to model the duration between transactions in a financial market. Even though there are developments in terms of modelling aspects, estimation, filtering and smoothing are still being investigated by researchers in this area. Almost all the existing procedures are highly computational intensive because of the complexity of the likelihood function. In this paper, we suggest a new procedure for estimation, filtering and smoothing in stochastic conditional duration models, based on estimating functions. Simulation studies indicate that the suggested procedure performs well and also fast in terms of computation. Copyright © 2016 John Wiley & Sons, Ltd.