Portfolio and investment risk analysis on global grids

The financial services industry today produces and consumes huge amounts of data and the processes involved in analysing these data have large and complex resource requirements. The need to analyse the data using such processes and get meaningful results in time, can be met only up to a certain extent by current computer systems. Most service providers attempt to increase efficiency and quality of their service offerings by stacking up more hardware and employing better algorithms for data processing. However, there is a limit to the gains achieved by using such an approach. One viable alternative would be to use emerging technologies such as the Grid. Grid computing and its application to various domains have been actively studied by many groups for more than a decade now. In this paper we explore the use of the Grid in the financial services domain; an area which we believe has not been adequately looked into.

[1]  Stavros A. Zenios,et al.  High-performance computing in finance: The last 10 years and the next , 1999, Parallel Comput..

[2]  D. Duffie,et al.  An Overview of Value at Risk , 1997 .

[3]  Jason Lee,et al.  High-Performance Remote Access to Climate Simulation Data: A Challenge Problem for Data Grid Technologies , 2001, ACM/IEEE SC 2001 Conference (SC'01).

[4]  Steven Bird,et al.  Grid-Enabling Natural Language Engineering By Stealth , 2003, HLT-NAACL 2003.

[5]  David Abramson,et al.  Research from the Trenches: Nimrod-G Resource Broker for Service-Oriented Grid Computing , 2001, IEEE Distributed Syst. Online.

[6]  Angelos Bilas,et al.  Parallelization and Performance of Portfolio Choice Models , 2001 .

[7]  A. Michaelides,et al.  Parallelization, optimization, and performance analysis of portfolio choice models , 2001, International Conference on Parallel Processing, 2001..

[8]  Jacek Gondzio,et al.  High-Performance Computing for Asset-Liability Management , 2001, Oper. Res..

[9]  Rajkumar Buyya,et al.  Alchemi: A .NET-based Enterprise Grid Computing System , 2005, International Conference on Internet Computing.

[10]  Ian T. Foster,et al.  GRUBER: A Grid Resource Usage SLA Broker , 2005, Euro-Par.

[11]  Carl Kesselman,et al.  GriPhyN and LIGO, building a virtual data Grid for gravitational wave scientists , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[12]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[13]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[14]  Rajkumar Buyya,et al.  A Grid service broker for scheduling e‐Science applications on global data Grids , 2006, Concurr. Comput. Pract. Exp..

[15]  Rajkumar Buyya,et al.  The Australian BioGrid Portal: Empowering the Molecular Docking Research Community , 2005 .

[16]  Carl Kesselman,et al.  High-Performance Remote Access to Climate Simulation Data: A Challenge Problem for Data Grid Technologies , 2001, ACM/IEEE SC 2001 Conference (SC'01).

[17]  David F. Snelling,et al.  UNICORE: uniform access to supercomputing as an element of electronic commerce , 1999, Future Gener. Comput. Syst..

[18]  David Abramson,et al.  Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids , 2005, Concurr. Comput. Pract. Exp..

[20]  Charles Slocomb Proceedings of the 2001 ACM/IEEE conference on Supercomputing, Denver, CO, USA, November 10-16, 2001, CD-ROM , 2001, SC.

[21]  Rajkumar Buyya,et al.  Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids: Research Articles , 2005 .

[22]  Ian T. Foster,et al.  Condor-G: A Computation Management Agent for Multi-Institutional Grids , 2004, Cluster Computing.

[23]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.