Year: 2021
Type: article
Abstract: The main object of investigation in this paper is a very general regression model in optional setting – when an observed process is an optional semimartingale depending on an unknown parameter. It is well known that statistical data may present an information flow/filtration without ‘usual conditions’. The estimation problem is achieved by means of structural least squares (LS) estimates and their sequential versions. The main results of the paper are devoted to the strong consistency of such LS-estimates. For sequential LS-estimates, the property of fixed accuracy is proved. Finally, several illustrative examples from risk theory and mathematical finance are presented.
Source: Statistics
Authors: Mohamed Abdelghani, Alexander Melnikov, Andrey Pak
Institutions: Morgan Stanley (United States), University of Alberta
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
Award: G170426675
OpenAlex ID: W3137291367