Use of eCommons for rapid dissemination of COVID-19 research
In order to maximize the discoverability of COVID-19 research, and to conform with repository best practices and the requirements of publishers and research funders, we provide special guidance for COVID-19 submissions.
|dc.description.abstract||This is an attempt to put to the disposition of the public the application of the theory behind Neural Networks General Hetero Scedascticity models. Its purpose is to model the discrepancies observed beween two same sector stocks whose short term behavior should be equivalent, due to heteroscedasticity, meaning the influence of a past chock on the future performance of a stock. Exploiting these arbitrages might provide incentives for day traders to short and buy stocks. We have written an application which applies most of the theory behind NN and Garch Models and in particular: Financial statistical modelling with a new nature-inspired technique Nikos S. Thomaidis_1, George D. Dounias1, and Nick Kondakis1,2. However, we would have required more time to complete this work. It is therefore an attempt which should be continued and developed so as to provide efficient information to the public of investors.||en_US|
|dc.type||dissertation or thesis||en_US|