Today’s forecast for U.S. Treasury yields is based on the August 23, 2012 constant maturity Treasury yields that were reported by the Board of Governors of the Federal Reserve System in its H15 Statistical Release at 4:15 p.m. Eastern Daylight Time August 24, 2012. The “forecast” is the implied future coupon bearing U.S. Treasury yields derived using the maximum smoothness forward rate smoothing approach developed by Adams and van Deventer (Journal of Fixed Income, 1994) and corrected in van Deventer and Imai, Financial Risk Analytics (1996). For an electronic delivery of this interest rate data in Kamakura Risk Manager table format, please subscribe via firstname.lastname@example.org.
The “forecast” for future U.S. dollar interest marginal bank funding costs is derived from the maximum smoothness forward rate approach, but it is applied to the forward credit spread between estimated bank funding costs (using inputs also reported in the H15 release) and the U.S. Treasury curve instead of to the absolute level of forward rates for the marginal bank funding cost curve.
This week’s projections for the 1 month Treasury bill rate (investment basis) see a return to the levels projected two weeks prior. This includes a downward shift in the intermediate and long term projections compared to last week, with the greatest deviation occurring in June 2017 at 27.3 basis points. The short term projections are smoother, and have a local peak in August 2013 of 30 basis points. The projected 1 month rate of 3.308% in July 2022 is down 14.5 basis points from last week. These trends are illustrated in the graph below. The 10 year U.S. Treasury yield is projected to rise steadily to reach 3.381% on July 31, 2022, 19.1 basis points lower than projected last week.
The second curve we forecast is the estimated marginal cost of funds curve for major bank holding companies. The marginal cost of funds is used by nearly every sophisticated banking firm to assign a marginal cost of funds on a matched maturity basis to each asset and a marginal credit for funds for each liability. This “transfer pricing” process has been best practice performance measurement in the banking industry since the first transfer pricing system was developed at Bank of America in San Francisco in 1973.
The marginal cost of funds for each banking company is unique and largely observable only by the bank itself. Bank funding costs are often estimated using two different inputs from the Federal Reserve H15 statistical release as proxies for the unobservable marginal cost of new funding for banks. Short term funding costs are estimated using short term Eurodollar funding costs. Long term funding costs are calculated using interest rate swap levels as proxies for a new issue of bonds on a non-call basis of $100 million or more. As we show below, this proxy curve understates the true marginal cost of funds for banking companies for three reasons. First, interest rate swaps contain no exchange of principal, but a new issue of bonds, of course, does. Second, bonds have “regular” credit risk with the potential loss of 100% of principal, but interest rate swaps suffer the risk only of interim changes in swap value if there is a counterparty default. Third, to the extent that the H15 interest rate swap data assumes a floating rate tied to Libor, the downward manipulation of Libor during the period from 2005 onward would artificially depress the fixed rate side of the transaction as well. We use no other securities tied to Libor because of the manipulation of the index. We explicitly reject the use of futures, caps and floors tied to a manipulated index, and we encourage the Federal Reserve to replace Libor-linked interest rate swap quotes with OIS swap quotes in its H15 statistical release. The analysis of future movements of the marginal cost of funds curve for banks is not intended to be a valuation yield curve for interest rate derivatives on the manipulated Libor index. Such a curve can only be accurately estimated by an organization participating in the manipulation process.
One of the problems in using U.S. dollar interest rate swap spreads as a proxy for bank funding costs is their unrealistic long term levels. U.S. dollar swap spreads this week were a negative 23 basis points at 30 years, 3 basis points less negative than last week. Today’s forecast for the marginal cost of bank funding continues to imply a rise and fall of spreads between the bank funding curve and U.S. Treasuries, with a local peak in the 1 month spread of 74.8 basis points in December 2012, 5.7 basis points higher than the previous forecast. Thereafter, bank funding spreads drop to a negative spread of 56.2 basis points in July 2013. This extreme gyration is generated by the difference in credit risk on the short end of the curve (where Eurodollar rates are used for 1 month, 3 months, and 6 months) and the interest rate swap rates used as proxies for the bank cost of funds (used for one year and beyond). As mentioned above, buyers of Eurodollar deposits run the risk of 100% loss of principal, but swap market participants stand to lose only the present value of the difference in original swap cash flows versus current market rates if the swap counterparty defaults with no collateral. Kamakura’s analysis ignores this credit differential, consistent with common market practice in estimating the marginal cost of bank funding in the absence of new issue quotations. One month spreads to US Treasuries continue to rise and fall thereafter, turning negative again in mid-2020 and beyond. The bank funding curve itself shows a peak in 1 month funding costs at 90.1 basis points in December 2012 and a fall to minus 27 basis points in July 2013. These gyrations too are caused by the differential in credit risk between the first six months of the curve and longer maturities.
These negative spreads between the U.S. dollar bank funding curve and U.S. Treasury yields reflect the blurring of credit quality between these two yield curves. The U.S. government has not been seen as risk free for some time by market participants. Similarly, more than 300 U.S. banking organizations received government funding during the period from 2008 onward under the troubled asset relief program and a number of other short and long term emergency funding programs.
Background Information on Input Data and Smoothing
The Federal Reserve H15 statistical release is available here:
The maximum smoothness forward rate approach to yield curve smoothing was described in this blog entry:
van Deventer, Donald R. “Basic Building Blocks of Yield Curve Smoothing, Part 10: Maximum Smoothness Forward Rates and Related Yields versus Nelson-Siegel,” Kamakura blog, www.kamakuraco.com, January 5, 2010. Redistributed on www.riskcenter.com on January 7, 2010.
The use of the maximum smoothness forward rate approach for bond data is discussed in this blog entry:
van Deventer, Donald R. “Basic Building Blocks of Yield Curve Smoothing, Part 12: Smoothing with Bond Prices as Inputs,” Kamakura blog, www.kamakuraco.com, January 20, 2010. Redistributed on www.riskcenter.com on January 21, 2010.
For a pictorial history of daily U.S. Treasury forward rates since January 2, 1962, see
Dickler, Daniel T., Robert A. Jarrow and Donald R. van Deventer, “Inside the Kamakura Book of Yields: A Pictorial History of 50 Years of Daily U.S. Treasury Forward Rates,” Kamakura Corporation memorandum, September 13, 2011.
For a quantitative summary of forward rate curve shapes that have prevailed since January 2, 1962, see
Dickler, Daniel T. and Donald R. van Deventer, “Inside the Kamakura Book of Yields: An Analysis of 50 Years of Daily U.S. Treasury Forward Rates,” Kamakura blog, www.kamakuraco.com, September 14, 2011.
The reasons for smoothing forward credit spreads instead of the absolute level of forward rates for the marginal bank funding cost curve were discussed in this blog entry:
van Deventer, Donald R. “Basic Building Blocks of Yield Curve Smoothing, Part 13: Smoothing Credit Spreads,” Kamakura blog, www.kamakuraco.com, April 7, 2010. Redistributed on www.riskcenter.com, April 14, 2010.
The Kamakura approach to interest rate forecasting was introduced in this blog entry:
Today’s Kamakura U.S. Treasury Yield Forecast
The Kamakura 10 year monthly forecast of U.S. Treasury yields is based on this data from the Federal Reserve H15 statistical release:
The graph below shows in 3 dimensions the movement of the U.S. Treasury yield curve 120 months into the future at each month end:
These yield curve movements are consistent with the continuous forward rates and zero coupon yields implied by the U.S. Treasury coupon bearing yields above:
In numerical terms, forecasts for the first 60 months of U.S. Treasury yield curves are as follows:
The forecasted yields for months 61 to 120 are given here:
Today’s Kamakura Forecast for U.S. Dollar Marginal Cost of Bank Funding
Today’s forecast for the U.S. Dollar marginal cost of bank funding is based on the following data from the H15 Statistical Release published by the Board of Governors of the Federal Reserve System:
Applying the maximum smoothness forward rate smoothing approach to the forward credit spreads between the proxy marginal cost of bank funding curve and the U.S. Treasury curve results in the following zero coupon bond yields:
The forward rates for the marginal cost of bank funding and U.S. Treasury curve are shown here:
The 10 year forecast for U.S. dollar marginal cost of bank funding is shown in the following graph:
The 10 year forecast for U.S. dollar marginal cost of bank funding spreads to U.S. Treasury yields is given in the following graph:
The numerical values for the implied future U.S. dollar marginal cost of bank funding spreads to U.S. Treasury yields are given here for 60 months forward:
The numerical values for the implied future U.S. dollar marginal cost of bank funding spreads to U.S. Treasury yields are given here for 61-120 months forward:
For more information about the yield curve smoothing and simulation capabilities in Kamakura Risk Manager, please contact us at email@example.com. Kamakura interest rate forecasts are available in pre-formatted Kamakura Risk Manager data base format.
Taqui Raza, Donald R. van Deventer and Martin Zorn
Honolulu, August 24, 2012
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