Towards financial cloud framework - modelling and benchmarking of financial assets in public and private clouds
Chang, Victor, Wills, Gary and De Roure, David (2010) Towards financial cloud framework - modelling and benchmarking of financial assets in public and private clouds. In: IEEE Cloud 2010, the Third International Conference on Cloud Computing, 5-10 Jul 2010, Miami, Florida, USA. (Unpublished)
Preview |
PDF (Poster)
(ITEM_5625)_Published_version_CHANG_2010_BUS.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (50kB) |
Abstract
Literature identifies two problems in clouds: (i) there are few financial clouds and (ii) portability of financial modelling from desktop to cloud is challenging. To address these two problems, we propose the Financial Cloud Framework (FCF), which contains business models, forecasting, sustainability, modelling, simulation and benchmarking of financial assets. We select Monte Carlo Methods for pricing and Black Scholes Model for risk analysis. Our objective is to demonstrate portability, speed, accuracy and reliability of financial models in the clouds, and present how modelling, simulation and benchmarking fit into FCF. Experiments and benchmark are performed in public and private clouds, where portability, speed, accuracy and reliability from desktop to clouds are successfully demonstrated.
Item Type: | Conference or Conference Paper (Poster) |
---|---|
Uncontrolled Keywords: | financial cloud framework, Monte Carlo Method, Black Scholes Model, modelling and simulation of financial assets |
Subjects: | H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Faculty / School / Research Centre / Research Group: | Faculty of Business > Department of Systems Management & Strategy |
Related URLs: | |
Last Modified: | 14 Oct 2016 09:14 |
URI: | http://gala.gre.ac.uk/id/eprint/5625 |
Actions (login required)
View Item |
Downloads
Downloads per month over past year