Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- PART I DATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK
- 1 Systemic Risk Information Requirements: Current Environment, Needs, and Approaches for Development
- 2 Aligning Models and Data for Systemic Risk Analysis
- 3 Applying FpML
- 4 Data Integration for Systemic Risk in the Financial System
- 5 Semantics in Systemic Risk Management
- PART II STATISTICS AND SYSTEMIC RISK
- PART III MEASURING AND REGULATING SYSTEMIC RISK
- PART IV NETWORKS
- PART V SYSTEMIC RISK ANDMATHEMATICAL FINANCE
- PART VI COUNTERPARTY RISK AND SYSTEMIC RISK
- PART VII ALGORITHMIC TRADING
- PART VIII BEHAVIORAL FINANCE: THE PSYCHOLOGICAL DIMENSION OF SYSTEMIC RISK
- PART IX REGULATION
- PART X COMPUTATIONAL ISSUES AND REQUIREMENTS
- PART XI ACCOUNTING ISSUES
- References
4 - Data Integration for Systemic Risk in the Financial System
from PART I - DATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK
Published online by Cambridge University Press: 05 June 2013
Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- PART I DATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK
- 1 Systemic Risk Information Requirements: Current Environment, Needs, and Approaches for Development
- 2 Aligning Models and Data for Systemic Risk Analysis
- 3 Applying FpML
- 4 Data Integration for Systemic Risk in the Financial System
- 5 Semantics in Systemic Risk Management
- PART II STATISTICS AND SYSTEMIC RISK
- PART III MEASURING AND REGULATING SYSTEMIC RISK
- PART IV NETWORKS
- PART V SYSTEMIC RISK ANDMATHEMATICAL FINANCE
- PART VI COUNTERPARTY RISK AND SYSTEMIC RISK
- PART VII ALGORITHMIC TRADING
- PART VIII BEHAVIORAL FINANCE: THE PSYCHOLOGICAL DIMENSION OF SYSTEMIC RISK
- PART IX REGULATION
- PART X COMPUTATIONAL ISSUES AND REQUIREMENTS
- PART XI ACCOUNTING ISSUES
- References
Summary
Abstract The need to integrate data from many sources is likely to be a key bottleneck in the analysis of systemic risk. Oversimplified approaches result in large amounts of code being written and maintained by hand, and many software engineers having access to sensitive data. We describe the separate dimensions of the problem – many “silver bullets” only address one or two. We explain why it is neither feasible nor desirable to use a single standard for all data sharing for financial research, and then explore the pros and cons of standards at different levels of detail. We then explore some of the tools needed to implement and evolve a set of data exchanges. Finally, we discuss how data difficulties raise important research issues for both financial analysis experts and computer scientists.
The systemic risk data integration challenge
Systemic risk analysis in the financial sector requires massive data integration. Information must be collected from hundreds (and ultimately, perhaps, thousands) of financial firms, corresponding entities must be matched, and all the data must be transformed to meet the needs of systemic risk analysis models. The Dodd–Frank act established an Office of Financial Research (OFR) and gave it the mission of collecting, integrating, and analyzing diverse data in order to better track and analyze financial systemic risk. The data integration challenge is daunting and, if handled poorly, will be a significant impediment to the OFR being able to do systemic analyses.
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- Handbook on Systemic Risk , pp. 93 - 122Publisher: Cambridge University PressPrint publication year: 2013
References
Army (2009). US Army Report and Message Formats. Department of Army (Headquarters) FM 6–99.2 (FM 101–5–2).
, , and (2005). Schema and ontology mapping with COMA++. In SIGMOD 2005: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, ACM, 906–908.Google Scholar
and (1995). Migrating Legacy Systems: Gateways, Interfaces & the Incremental ApproachMorgan Kaufmann.Google Scholar
, , and (2009). Exploring schema repositories with Schemr. In ACM SIGMOD/PODS 2009 Conference, Providence, Rhode Island, June 30 – July 2.Google Scholar
and (2002). COMA – A system for flexible combination of schema matching approaches. In VLDB 2002, Proceedings of 28th International Conference on Very Large Data Bases, Hong Kong, China, August 20–23.Google Scholar
, , and (2003). Learning to match the schemas of databases: a multistrategy approach. Machine Learning, 50 (3) 279–301.CrossRefGoogle Scholar
, , , and Ontology mapping: a machine learning approach. In Handbook on Ontologies, and (eds), Springer, 385–404.
, , , and (2005). Data exchange: semantics and query answering. Theoretical Computer Science, 336 (1) 89–124.CrossRefGoogle Scholar
, , and (2005). Data exchange: getting to the core. ACM Trans. Database Syst., 30 (1) 174–210.CrossRefGoogle Scholar
(2005). Gartner says more than 50 percent of data warehouse projects will have limited acceptance or will be failures through 2007. http://www.gartner.com/ it/page.jsp?id=492112.Google Scholar
, , and (1994). Context interchange: overcoming the challenges of large-scale interoperable database systems in a dynamic environment. In Proceedings of the Third International Conference on Information and Knowledge Management (CIKM 1994), ACM, 337–346.Google Scholar
, , , and (1999). Context interchange: new features and formalisms for the intelligent integration of information. ACM Transactions on Information Systems 17 (3) 270–293.CrossRefGoogle Scholar
(2007). Beauty and the beast: the theory and practice of information integration. In 11th International Conference on Database Theory (ICDT 2007), 28–43.Google Scholar
, , and (2001). Generic schema matching with Cupid. In VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases, Rome Italy, September 11–14. Morgan Kaufmann, 49–58.Google Scholar
, , , , , and (2007). Web-scale data integration: you can afford to Pay as You Go. In CIDR 2007, Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 7–10. Online Proceedings www.cidrdb.org/cidr2007.Google Scholar
, , , , , , and (2009). Galaxy: encouraging data sharing among sources with schema variants. Demo at IEEE International Conference on Data Engineering, Shanghai, China, March 29 – April 2 IEEE.Google Scholar
, , , , and (2008). The Harmony Integration Workbench. Journal on Data Semantics 11 65–93.Google Scholar
(1996). Panorama: A database system that annotates its answers to queries with their properties. Journal of Intelligent Information Systems 7 (1) 51–73.CrossRefGoogle Scholar
, and (2001). A survey of approaches to automatic schema matching. The VDLB Journal 10 (4) 334–350.Google Scholar
, , and (2013). Fit for purpose: towards an engineering basis for data exchange standards. In IWEI 2013, IFIP Working Conference on Enterprise Interoperability, Enschede, The Netherlands, March 27–28.Google Scholar
, , and (2004). From semantic integration to semantics management: case studies and a way forward. SIGMOD Record 33 (4) 44–50.CrossRefGoogle Scholar
, , and (1994). Using semantic values to facilitate interoperability among heterogeneous information systems. ACM Transactions on Database Systems 19 (2) 254–290.CrossRefGoogle Scholar
, , , , , , , , , and (2010). OpenII: an open source information integration toolkit. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6–10.Google Scholar
, , , , , , and (2010). Exploring schema similarity at multiple resolutions. In Proceedings of the ACM SIG-MOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6–10.Google Scholar
and (2006). Schema summarization. In Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, September 12–15. ACM. http://www.pubzone.org/dblp/conf/vldb/YuJ06a.Google Scholar
and (2011). Quality of data standards: framework and illustration using XBRL taxonomy and instances. Electronic Markets 21 (2) 129–139.CrossRefGoogle Scholar
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