Abstract
Mathematics and finance interrelated deeply in the last 60 years since Markowitz’s seminal work on portfolio selection theory in 1952. In the chapter I briefly explore some fundamental quantitative problems associated with five modern risk areas: the fair value of credit, debt, funding and capital risk, collectively known as XVA risk; operational risk, fair lending risk, financial crimes risk, and finally model risk. The problems analyzed fall both in the category of “old with exposed flaws” as well as “new and in search of new tools”. While it is not intended as a comprehensive list of all of the quantitative problems facing the industry today, however, these problems have emerged post-crisis and have found themselves on the top of many firm and regulatory agendas in many respects.
The views expressed in this document are the author’s and do not reflect Wells Fargo’s opinion or recommendations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abasto, Damian and Kust, Mark P., “Model01: Quantifying the Risk of Incremental Model Changes”, September 5, 2014. Available at SSRN: http://ssrn.com/abstract=2492256 or 10.2139/ssrn.2492256.
Basel Committee on Banking Supervision (BCBS), “International Convergence of Capital Measurement and Capital Standards A Revised Framework Comprehensive Version”, June 2006 http://www.bis.org/publ/bcbs128.pdf.
Basel Committee on Banking Supervision (BCBS), “Observed range of practice in key elements of Advanced Measurement Approaches (AMA)”, July 2009 http://www.bis.org/publ/bcbs160b.pdf.
Basel Committee on Banking Supervision (BCBS), “Operational Risk – Supervisory Guidelines for the Advanced Measurement Approaches”, June 2011 http://www.bis.org/publ/bcbs196.pdf.
Basel Committee on Banking Supervision (BCBS), “Basel III: A Global Regulatory Framework for more Resilient Banks and Banking Systems (Revised)”, June 2011 http://www.bis.org/publ/bcbs189.pdf.
Berkane, Maia. Wells Fargo & Co., Private Communication, March 2015.
Bhattacharyya, Siddhartha., Jah, Sanjeev., Tharakunnel, Kurian., Westland, J. Christopher., “Data Mining for Credit Card Fraud: A Comparative Study”, Decision Support Systems, 50(3), February 2011, 602–613.
Bolton, Richard J., and David J. Hand, “Statistical Fraud Detection: A Review”, Statistical Science, 17(3), 2002, 235–249.
Chan, P.K., Fan, W., Prodromidis, A.L., Stolfo, S.J., “Distributed Data Mining in Credit Card Fraud Detection”, Data Mining, (November/December), 1999, 67–74.
Chen, R.C., Chen, T.S., Lin, C.C., “A New Binary Support Vector System for Increasing Detection Rate of Credit Card Fraud”, International Journal of Pattern Recognition, 20(2), (2006), 227–239.
Cont, Rama. “Model Uncertainty and Its Impact on the Pricing of Derivative Instruments”, Mathematical Finance, 16, July 2006.
Derman, E, “Model Risk”, Risk, 9(5), 139–145, 1996
Glasserman, P., and Xu, X. “Robust Risk Measurement and Model Risk”, Journal of Quantitative Finance, 2013.
Grocer, Stephen, “A List of the Biggest Bank Settlements.” The Walls Street Journal, 23 June 2014.
Hoeting, J., Madigan, A.D, Raftery, A. E, Volinsky, C. T., “ Bayesian Model Averaging: A Tutorial”, Statistical Science 14(4), 382–417.
Jorion, Philippe. GARP (Global Association of Risk Professionals) (2009-06-08). Financial Risk Manager Handbook (Wiley Finance) Wiley. Kindle Edition.
Kenyon, Chris., Stamm, Roland., “Discounting, Libor, CVA and Funding: Interest Rate and Credit Pricing”. Houndmills, Basingstoke: Palgrave Macmillan, 2012. Print.
Morini, Massimo., “Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators”, Hoboken: Wiley 2011
Ngai, W.T., Hu, Yong., Wong, Y. H., Chen, Yijun., Sun, Xin. “The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature.” Decision Support Systems 50, 3 (February 2011), 559–569.
OCC Bulletin 2011-12/Federal Reserve Bulletin SR 11-7, “Supervisory Guidance on Model Risk Management”, April 4, 2011. http://www.occ.gov/news-issuances/bulletins/2011/bulletin-2011-12a.pdf.
Platt, J.C., “Fast Training of Support Vector Machines Using Sequential Minimal Optimization”, in: B. Scholkopf, C.J.C. Burges, A.J. Smola (Eds.), Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge, MA, 1998, 185–208.
Raftery, A. E., “Bayesian Model Selection in Structural Equation Models”, in Testing Structural Equation Models, K. Bollen and J. Long, eds. Newbury Park, CA: Sage, 1993 163–180.
Rebonato, R, “Theory and Practice of Model Risk Management”, in Modern Risk Management: A History; RiskWaters Group, 2003. 223–248.
Ross, S. L., Yinger, J., “The Color of Credit: Mortgage Discrimination, Research Methodology, and Fair-Lending Enforcement”. Cambridge, Mass: MIT Press, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Oden, K.D. (2017). Quantitative Finance in the Post Crisis Financial Environment. In: Tian, W. (eds) Commercial Banking Risk Management. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-137-59442-6_16
Download citation
DOI: https://doi.org/10.1057/978-1-137-59442-6_16
Published:
Publisher Name: Palgrave Macmillan, New York
Print ISBN: 978-1-137-59441-9
Online ISBN: 978-1-137-59442-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)