GRID2006
3-4 February 2006
Palermo, Italy
published May 21, 2007
Palermo, Italy
published May 21, 2007
Grid technology has been designed to support the solution of some computational and data grand challenges offered by several scientific fields such as the one in physics, astronomy, meteorology, etc. The success of these efforts suggest the Grid computing as a promising path for solving computationally intensive real- time and near real - time financial problems. Although existing applications in the financial industry are still in the start - up stage, the potential of Grid computing seems endless. It is likely that in the next decades financial institutions will increasingly compete on the efficient allocation of computing power by maintaining the current high- security standards. Since the Grid helps dealing with computationally intensive problems with distributed data, this technology is best suited for suppor ting the coming challenges of financial institutions. The Grid in Finance 2006 - Workshop is intended to be an international forum for researchers from academia and industry in the area of Grid - based applications in finance.
Editorial Board
Cozzini Stefano (chairman), d'Addona Stefano, Mantegna Rosario
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| Preface |
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Preface
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| Conference contributions |
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Multi-lingual sentiment analysis of financial news streams
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Financial data tombs and nurseries: A grid-based text and ontological analysis
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Scalable architectures for responsive auctions
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Financial Risk Management Via Multi Model Inference GRID Applications
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Grid solution for market and counterparty risk calculation. Real life problems from the point of view of the system developer.
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Time Varying Sensitivities on a GRID architecture
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Real-time grid computing for financial applications
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The new EGRID infrastructure
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Financial Services GRiD Virtualization for Increased Business Performance and Lower TCO
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Grid Based Full Portfolio Revaluation for VaR
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City@home: Monte Carlo derivative pricing distributed on networked computers
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Atos Origin
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LCG-RUS: Aggregative Accounting Service Enabling Economic Modelling for Commercial Grid
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Internal Grid Comouting Experciences in Financial Companies
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Grid Services for Derivatives Pricing
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High Frequency Financial Data in Egrid
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