James Vickery is at the NY Fed in the Research and Statistics Group and is the author or coauthor on many valuable Federal Reserve publications relevant to Net Interest Margin Optimization and generally to the domestic and global banking system. The Federal Reserve plays a significant role in regulating, defining, monitoring, and reporting domestic banking activities. Three important pieces of Federal Reserve legislation significantly determine the structure of the current domestic banking system: Bank Holding Company Act of 1956; Financial Services Modernization Act of 1999, commonly called Gramm-Leach-Bliley; and Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010. The Bank Holding Company Act and Gramm-Leach-Bliley allows the aggregation of $15+T of assets and liabilities onto the balance sheets of a relatively few Bank Holding Companies (BHCs). Post the Global Credit Crisis, Dodd-Frank introduced constraints on BHC proprietary trading and mandated increased transparency on the composition of BHC banking books. Think of it like Gramm-Leach-Bliley pulled all the assets together and Dodd-Frank made the assets simple and published them.  If the Net Interest Margin Optimization  was like a standard applied math simulation like Taylor-Couette flow, the Federal Reserve would: regulate the speeds of the rotating cylinders to avoid turbulence,  define the Reynolds number, monitor the Navier-Stokes simulation stability/convergence, and  be the primary source for mandatory quarterly historical data reporting. The interesting part is that advances in technology, quantitative financial modeling, and stochastic programming have interleaved with evolving banking legislation to make NIM Optimization feasible (see Richard Fairbank) on a large scale. Vickery is the talented tour guide to the Federal Reserve Bank activities, legislation, and data infrastructure.

Through the lens of Net Interest Margin Optimization, I am particularly interested in four publications from Vickery’s NYFRB website:

1. A Structural View of U.S. Bank Holding Companies, Jul. 2012.
2. Do Big Banks Have Lower Operating Costs, Dec. 2014.
3. Available For Sale? Understanding Bank Securities Portfolios, Feb. 2015.
4. A Look at Bank Loan Performance, Oct. 2013.

The first paper describes the organizational structure of contemporary Bank Holding Companies. Complex BHCs can hold thousands of subsidiaries, but the majority of the assets are held in the commercial banking subs. The paper points out for example that JPM controls 3300+ subs but only four are domestic commercial banks. The four subs hold 86% of JPM’s assets. Appendix A in the paper provides an enumeration and synopsis of the BHC structure and banking book composition reports. The second paper, Do Big Banks Have Lower Operating Costs shows that the Non-Interest Expense ratios are inversely proportional to the size of the assets held by the respective banks. The reason to study this problem is to provide evidence of economies of scale in commercial banking. The form of the research is interesting and the references include  Cornett, McNutt, and Tehranian’s 2006  paper Performance Change Around Bank Mergers: Revenue Enhancement versus Cost Reductions. This reference is obviously relevant to the possibility of optimized NIM revenue scaling with the size of the bank. The third blog post on the Available For Sale portfolio examines the security composition of the typical banking book. Contemporary bank accrual portfolios contain more complex securities than simply deposits, Fed funds, Cards, and Loans. Only some of these banking book instruments are Hold To Maturity (HTM) a significant fraction are Available For Sale (AFS). AFS Securities add complexity and runtime to the Net Interest Margin simulation and optimization process. The last blog post takes a cross sectional tour of the historical impairment write off of Bank loan portfolios since the Global Credit Crisis. The aggregated data is taken from the Quarterly Trends for Consolidated U.S. Banking Organizations. For the Net Interest Margin optimization problem this draws attention for a couple reasons. One, the Fed is almost certainly approaching all the the analysis from a risk perspective. With NIM Optimization we analyze this data from the P&L perspective, of which risk is a component. Two, the NIM P&L attribution model has to pay attention to the Credit failure to pay model in addition the fluctuation of interest and exchange rates. The credit spread to the funding rate has to be one of the major P&L drivers of the banking book.  Similarly the correlation of default (failure to pay) between loans must play a significant role in any simulation. Failure to account for the relevant risk factors can lead to a downward biased estimate of tail losses, similar to what happened to the polls in the recent U.S. election (at the Princeton Election Consortium or 538 see GLL) or in the Credit Crisis (see MacKenzie, MacKenzie and Spears, or Salmon). The current practice of loan loss provisioning  will be a subject we come back to.