Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bb9c88b65-bcq64 Total loading time: 0 Render date: 2025-07-25T02:58:15.488Z Has data issue: false hasContentIssue false

2 - Aligning Models and Data for Systemic Risk Analysis

from PART I - DATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK

Published online by Cambridge University Press:  05 June 2013

By
Roger M. Stein
Edited by
Jean-Pierre Fouque  and
Joseph A. Langsam
Roger M. Stein
Affiliation:
7 World Trade Center, New York
Jean-Pierre Fouque
Affiliation:
University of California, Santa Barbara
Joseph A. Langsam
Affiliation:
University of Maryland, College Park
Get access

Summary

Abstract The recent financial crisis has brought to the fore issues of quantifying and reducing systemic risk. This focus has precipitated the exploration of various methods for measuring systemic risks and for attributing systemic risk contributions to systemically important financial institutions. Concomitant with this stream of research are efforts to collect, standardize and store data useful to these modeling efforts. While discussions of modeling approaches are pervasive in the literature on systemic risk, issues of data requirements and suitability are often relegated to the status of implementation details. This short chapter is an attempt to deepen this discussion. We provide a 2 × 2 mapping of modeling strategies to key data characteristics and constraints that can help modelers determine which models are feasible given the available data; conversely, the mapping can provide guidance for data collection efforts in cases where specific analytic properties are desired. The framework may also be useful for evaluating, at a conceptual level, the trade-offs for incremental data collection. To provide background for this mapping, we review the analytic benefits and limitations of using aggregate vs. micro-level data, provide background on the role of data linking and discuss some of the practical aspects of data pooling including concerns about confidentiality. Throughout the chapter, we include examples from various domains to make the points we outline concrete.

Introduction

Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination; that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue.

R.A. Fisher

Information

Type
Chapter
Information
Handbook on Systemic Risk , pp. 37 - 65
Publisher: Cambridge University Press
Print publication year: 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Abbe, E.A., Khandani, A.E., & Lo, A.W. (2011). Privacy-preserving methods for sharing financial risk exposures. MIT Sloan School of Management.Google Scholar
Aggarwal, C.C., & Yu, P.S. (eds.) (2008). Privacy-Preserving Data Mining: Models and Algorithms. Springer-Verlag.CrossRef
Abowd, J.M., & Vilhuber, L. (2005). The sensitivity of economic statistics to coding errors in personal identifiers. Journal of Business and Economic Statistics 23 (2) 133–165.CrossRefGoogle Scholar
Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. In SIGMOD 2000: Proceedings of the International Conference on Management of Data, 439–450. ACM.Google Scholar
Ainger, D.J., & Goldfeld, S.M. (1974). Estimation and prediction from aggregate data 64 Stein when aggregates are measured more accurately than their components. Econometrica 42 (1) 113–134.Google Scholar
Barnett, W.A., Diewertb, W.E., & Zellner, A. (2011). Introduction to measurement with theory. Journal of Econometrics 161 (1) 1–5.CrossRefGoogle Scholar
Bennett, M. (2011). The EDM Council Semantics Repository – Considerations in Ontology Alignment. EDM Council.Google Scholar
Bisias, D., Flood, M., Lo, A.W., & Stavros, V. (2012). A survey of systemic risk analytics. Working Paper, Office of Financial Research, Washington DC.Google Scholar
Blundell, R., & Stoker, T.M. (2005). Heterogeneity and aggregation. Journal of Economic Literature XLIII 347–391.Google Scholar
Castro, J. (2012). Recent advances in optimization techniques for statistical tablular data protection. European Journal of Operations Research 216 (2) 257–269.CrossRefGoogle Scholar
CFO Publishing LLC. (2011). A new role for the times: opportunities and obstacles for the expanding finance function. CFO Publishing LLC.
Chinchalkar, S., & Stein, R.M. (2010). Comparing loan-level and pool-level mortgage portfolio analysis. Moody's Research Labs.Google Scholar
Dijk, M. v., Gentry, C., Halevi, S., & Vaikuntanathan, V. (2010). Fully homomorphic encryption over the integers. In Advances in Cryptology – EUROCRYPT 2010, LNCS 6110, 24–43.Google Scholar
Duffie, D. (2010). Systemic risk exposures: a 10-by-10-by-10 approach. Graduate School of Business, Stanford University.Google Scholar
Dunn, H.L. (1946). Record Linkage. American Journal of Public Health 36 (12) 1412–1416.Google ScholarPubMed
Dwork, C. (2008). An ad omnia approach to defining and achieving private data analysis. In PinKDD'07: Proceedings of the 1st ACM SIGKDD International Conference on Privacy, Security, and Trust in KDD, 1–13. Springer-Verlag.Google Scholar
EDM Council. (2011). Semantics Repository. Retrieved from EDM Council web site: http://www.edmcouncil.org/sec_semantics.aspx.
Enders, W. (2009). Applied Econometric Time Series. Wiley.Google Scholar
Elliott, G., Granger, C.W.J., & Timmermann, A. (eds) (2006). Handbook of Economic Forecasting, vol. 1, Elsevier.
Fellegi, I., & Sunter, A. (1969). A theory for record linkage. Journal of the American Statistical Association 64 (328) 1183–1210.CrossRefGoogle Scholar
Flood, M. (2009). Embracing change: financial informatics and risk analytics. Quantitative Finance 9 (3).CrossRefGoogle Scholar
Fung, B.C., Wang, K., Chen, R., & Yu, P. S. (2010). Privacy-preserving data publishing: a survey on recent developments. ACM Computing Surveys 42 (4) Article No. 14.CrossRefGoogle Scholar
Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In STOC09169–178. ACM.Google Scholar
Gross, F. (2010) International reference data utility: a necessary infrastructure for measuring systemic risk. Presentation at the Measuring Systemic Risk Conference – The Milton Friedman Institute, Chicago. http://mfi.uchicago.edu/events/ 20101215_systemicrisk/ppts/12.16.2010_FGross.ppt.Google Scholar
Grunfeld, Y., & Griliches, Z. (1960). Is aggregation necessarily bad?The Review of Economics and Statistics 42 (1) 1–13.CrossRefGoogle Scholar
Hanson, S.G., Pesaran, M.H., & Schuermann, T. (2008). Firm heterogeneity and credit risk diversification. Journal of Empirical Finance 15 (4) 583–612.CrossRefGoogle Scholar
Hernandez, M.A., Ho, H., Koutrika, G., Krishnamurthy, R., Popa, L., Stanoi, I.R., Vaithyanathan, S., & Das, S. (2010). Unleashing the power of public data for financial risk measurment, regulation and governance. IBM Technical Report #RJ10475.
Herzog, T.N., Scheuren, F.J., & Winkler, W.E. (2007). Data Quality and Record Linkage Techniques. Springer-Verlag.Google Scholar
Kelejian, H.H. (1980). Aggregation and disaggregation of non-linear equations. In Evaluation of Econometric Models, J., Kmenta, & J., Ramsay (eds). Academic Press.Google Scholar
Kinlaw, W.B., Kritzman, M., & Turkington, D. (2011). Toward determining systemic im portance. MIT Sloan Research Paper No. 4940-11.
Kritzman, M., Li, Y., Page, S., & Rigobon, R. (2010). Principal components as a measure of systemic risk. MIT Sloan School of Management.Google Scholar
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007). L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1 (1) Article no. 3.CrossRefGoogle Scholar
Narayanan, A., & Shmatikov, V. (2008). Robust De-anonymization of large sparse datasets. (How to break anonymity of the Netflix prize dataset). In Proc. of 29th IEEE Symposium on Security and Privacy, 111–125). IEEE Computer Society.Google Scholar
Office of Financial Research. (2010). Office of Financial Research Statement on Legal Entity Identification For Financial Contracts. Retrieved from http://www.treasury. gov/initiatives/Documents/OFR-LEI_Policy_Statement-FINAL.PDF.
Rivest, R.L., Adleman, L., & Dertouzos, M.L. (1978). On data banks and privacy homomorphisms. In Foundations of Secure Computation, R., DeMillo, D., Dobkin, A., Jones, and R., Lipton (eds), 169–180, Academic Press.Google Scholar
Robinson, W. (1950). Ecological correlations and the behavior of individuals. American Sociological Review 15 (3) 351–357.CrossRefGoogle Scholar
Stein, R.M. (2012). The role of stress testing in credit risk management. Journal of Investment Management 10 (4) 64–90.Google Scholar
weeney, L. (2002). k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10 (5) 557–570.Google Scholar
van Garderen, K.J., Lee, K., & Pesaran, M.H. (2000). Cross-sectional aggregation of non linear models. Journal of Econometrics 95 (2) 285–331.CrossRefGoogle Scholar
Wang, H., & Liu, R. (2011). Privacy-preserving publishing microdata with full functional dependencies. Data & Knowledge Engineering 70 (3) 249–268.CrossRefGoogle Scholar
Winkler, W.E. (2006). Overview of record linkage and current research directions. Research Report, US Census Bureau, Statistical Research Division, Washington, DC.Google Scholar

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×