ABOUT THE AUTHOR

Donald R. Van Deventer, Ph.D.

Don founded Kamakura Corporation in April 1990 and currently serves as Co-Chair, Center for Applied Quantitative Finance, Risk Research and Quantitative Solutions at SAS. Don’s focus at SAS is quantitative finance, credit risk, asset and liability management, and portfolio management for the most sophisticated financial services firms in the world.

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Five of Seven Regional Banks Trade at Credit Spreads Better than the Too Big to Fail Banks

08/12/2014 12:28 PM

In this note, we present some initial evidence of whether or not there is a funding advantage for the “too big to fail banks.” We use the Kamakura U.S. Dollar Cost of Funds IndexTM, which represents the composite credit spreads of the four largest deposit-taking U.S. banks. Currently, four banking firms underlie the U.S. Dollar Cost of Funds Index: Bank of America (BAC), Citigroup (C), JPMorgan Chase & Co. (JPM), and Wells Fargo & Co. (WFC).

Conclusion: One day’s trading evidence is not definitive, but we find that traded credit spreads were lower than the U.S. Cost of Funds Index for five of the seven “regional” banks with traded bonds on August 11, 2014. One of the other two banks, Ally Financial (ALLY), had traded credit spreads well above the U.S. Cost of Funds Index, but Ally Financial itself was the beneficiary of a rescue by the U.S. government.

Step one in today’s analysis is to report the composite credit spreads of the four “too big to fail” banks underlying the U.S. Cost of Funds Index. The big four banks represented 4 of the 10 most heavily traded bond issuers in the United States in this chart of fixed-rate non-call senior debt issues. For information on bond types excluded from the construction of the index, please see the note on the index below. Here is a summary of today’s trading volume:

There were 1,040 trades in 144 bond issues representing $251.5 million in principal of the four reference banks underlying the U.S. Cost of Funds Index. There is variation in credit quality among the four banks, which we have noted in other posts. For purposes of this note, however, we focus on the composite index alone. The credit spreads fitted to the August 11, 2014 trade data are shown here:

Comparison of Regional Bank Credit Spreads with the
U.S. Cost of Funds Index, August 11, 2014

Next, we follow the same credit spread calculation procedures used in the U.S. Cost of Funds Index for seven banking firms which had observable bond trades on August 11, 2014. The total volume of trades in these seven banking firms was only one-sixth of the trading volume of the 4 reference banks. We rank the 7 banking firms by the difference between their credit spreads and the matched maturity credit spreads that would be consistent with the U.S. Cost of Funds Index exactly as graphed above.

For example, State Street Corporation (STT) had $10 million in trading volume in the 2.875% bonds due March 7, 2016. On a trade-weighted basis, these bonds had an average credit spread of 0.148%. The credit spread for the identical maturity from the U.S. Cost of Funds Index is shown in the chart as 0.550%. The actual credit spread for State Street Corporation was 0.402% lower than the composite credit spread for the 4 “too big to fail” banks. In the American Express (AXP) family, American Express Centurion Bank and American Express Co. itself traded at 0.421% and 0.217% below the U.S. Cost of Funds Index on August 11, 2014. U.S. Bancorp (USB) traded in small volume at 0.304% and 0.263% below in the index. KeyCorp (KEY) had a funding advantage of 0.222% on the 1.65% bonds issued by KeyBank N.A. and 0.012% on the 3.75% bonds issued in its own name. Regions Financial (RF) showed a premium of 0.389% versus the U.S. Cost of Funds Index on $1.9 million of trading volume. There were 3 issues by Ally Financial Inc., all of which traded at a premium of more than 1.20% over the U.S. Cost of Funds Index.

Is the “Liquidity” of Trading in the Too Big to Fail Banks a Competitive Advantage?
A careful reader would ask, “Does the fact that the regional banks are trading in such small volume mean that the too big to fail banks in fact have an advantage in funding access if not in funding pricing?” That is a very good question and a complicated question to answer. In the U.S. corporate bond market, both the very best credits and the very worst credits show next to no trading volume. The evidence on August 11, 2014 is shown in this graph of total daily trading volume as classified crudely by legacy credit ratings, with a rating of 1 being judged “best.”

It is the credit risk rankings from 6th to 10th where there is the biggest overlap of supply (institutions that bought the ABC Company bonds in the past and now want to sell) and demand (institutions who want to own more ABC Company bonds than they now have). Is the lack of trading volume a problem? Not for Exxon Mobil (XOM), with annualized default probabilities of 0.05% at 10 years. The trading volume in Exxon Mobil bonds on August 11, 2014 was 4 trades in 2 issues for a total principal amount of $220,000. Does that mean Exxon Mobil has no access to financing? Of course not. The March 17, 2014 bond offering by Exxon Mobil was a huge success and there is a scarcity of investors willing to sell the bonds and a lack of primary issuance by the company itself. The treasurer of Exxon Mobil has a much less challenging job than his counterparts at the big 4 banks underlying the U.S. Cost of Funds Index.

Conclusion and Next Steps
One day’s evidence is just one day’s evidence. In the weeks ahead we will revisit the funding cost differentials of the too big to fail banks versus their smaller competitors using the U.S. Cost of Funds Index as the proxy for their costs. We expect to see what we see in the August 11, 2014 data: the best credits will trade at lower spreads than the too big to fail banks. The too big to fail banks, even with implicit government support, are pushing the bond market so hard that there are many willing sellers of their bonds. Exxon Mobil is in a much better financial position than any of them, even with no implicit government support.

Background on the U.S. Cost of Funds Index TM
Measuring the marginal cost of funds for a financial institution is a critical calculation from both a management perspective and a regulatory perspective. From a management perspective, if one’s own firm faces a funding disadvantage versus a group of banks who are “too big to fail,” that has dramatic implications for corporate strategy. From a regulatory perspective, favoring a group of financial institutions with an implicit guarantee of survival can create an anti-competitive subsidy as an unintended consequence. In this section, we explain the U.S. Dollar Cost of Funds IndexTM from Kamakura Corporation which makes quantification of funding advantages and disadvantages a practical daily reality.

Background on the U.S. Dollar Cost of Funds Index
On a typical day in the U.S. corporate bond market, there are significant bond trades in fewer than 20 of the 6,730 banks insured by the U.S. Federal Deposit Insurance Corporation. Moreover, there have been trades in only 14 U.S. banking legal entities in the credit default swap market since July 2010. That means for the overwhelming majority of financial institutions, an arm’s length benchmark for marginal funding costs is the only alternative since there is literally no trading in their own name. For many years, the interest rate swap curve was used as this benchmark for transfer pricing, asset and liability management, and internal probability assessment by the majority of large banks. With the unveiling of the Libor scandal and the associated prolonged period of negative interest rate swap spreads to the U.S. Treasury yield curve, the swap curve has lost credibility as a reliable benchmark for the marginal cost of funds.

For this reason, a new benchmark is needed that has these characteristics:

  • It should realistically measure the marginal cost of new funds to the financial institutions with the largest trading volume.
  • It should be based on public information that is readily verifiable by any investor or other market participant.
  • It should be based on actual trades, not estimates of trading costs.
  • It should be a calculation that can be done without the input of information or the cooperation of any of the financial institutions named in Libor-related lawsuits.
  • It should be based on such a large volume of trades that manipulation of the benchmark would be difficult, if not impossible.
  • It should be a composite of data relating to a number of financial institutions so that no one institution’s cost of funds is used as the benchmark.
  • The calculation methodology should be transparent and replicatable by any third party.
  • The calculation agent should not be a legal entity or other organization directly or indirectly controlled by the group of financial institutions named in Libor-related lawsuits.

The U.S. Dollar Cost of Funds IndexTM from Kamakura Corporation
Kamakura Corporation has created just such an index, labeled the “U.S. Dollar Cost of Funds Index.TM” The U.S. Dollar Cost of Funds Index TM measures the trade-weighted cost of funds for the largest deposit-taking U.S. bank holding companies. The index is a credit spread, measured in percent and updated daily, over the matched maturity U.S. Treasury yield on the same day. The current bank holding companies used in determining the index are Bank of America Corporation (BAC), Citigroup Inc. (C), JPMorgan Chase & Co. (JPM), and Wells Fargo & Company (WFC). The index is an independent market-based alternative to the Libor-swap curve that has traditionally been used by many banks as an estimate of their marginal cost of funds. Kamakura Corporation is the calculation agent, and the underlying bond price data is provided by TRACE and the U.S. Department of the Treasury. The National Association of Securities Dealers launched the TRACE ( Trade Reporting and Compliance Engine) in July 2002 in order to increase price transparency in the U.S. corporate debt market. The system captures information on secondary market transactions in publicly traded securities (investment grade, high yield and convertible corporate debt) representing all over-the-counter market activity in these bonds. For each of the reference names used in the calculation of the index, Kamakura Corporation assembles bond data as follows:

  • All bond trades of that reference name are gathered from TRACE.
  • All bonds which are not fixed rate debt issues are dropped.
  • All bonds which are not “non-call” (other than “make whole” calls) are dropped
  • All bonds which do not pay interest semi-annually are dropped.
  • All “survivorship option” bonds are dropped.
  • All bonds with a changing interest rate or principal amount tied to any index are dropped. This eliminates structured products.

The result is a large number of straight non-call fixed rate senior debt issues which traded that day. This contrasts with the Libor calculation procedure in which there are zero trades for $0 on 0 instruments.

Assembling the Composite Index
The composite credit spread is then calculated as follows:

  1. The traded-weighted average yield is calculated for each of the underlying bonds.
  2. The matched-maturity U.S. Treasury yield is interpolated from the “on the run”
  3. maturities published by the U.S. Department of the Treasury.
  4. The trade-weighted credit spread for each issue is calculated by subtracting the interpolated matched maturity Treasury yield from the trade-weighted average yield.
  5. Since the spread varies by years to maturity, a curve is fit to all observable bonds using a cubic function of the years to maturity t on each issue.

Credit spread = a + bt + ct2 + dt3

The coefficients a, b, c, and d are calculated using ordinary least squares

The terms, trading volume, and pricing for each underlying bond, the matched maturity U.S. Treasury yields, the coefficients of the index function for that day, and the “on the run” values of the index are provided by Kamakura Corporation to index clients.

 

ABOUT THE AUTHOR

Donald R. Van Deventer, Ph.D.

Don founded Kamakura Corporation in April 1990 and currently serves as Co-Chair, Center for Applied Quantitative Finance, Risk Research and Quantitative Solutions at SAS. Don’s focus at SAS is quantitative finance, credit risk, asset and liability management, and portfolio management for the most sophisticated financial services firms in the world.

Read More

ARCHIVES