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

SAS Weekly U.K. Gilt Yield and Pound Sterling Forecast, October 11, 2024: Probability of Inverted Yields at 26.4%

10/14/2024 06:54 AM

Summary

  • The 2-year/10-year United Kingdom Gilt spread closed the week at a positive 4.3 basis points.
  • As a result, today’s simulation shows that the probability of future negative Gilt spreads in the 91-day period ending April 11, 2025 is 26.4%.
  • The most likely one percent range for the 3-month yield in ten years is unchanged: zero to 1%. The most likely one percent range for the 10-year yield ten years forward is 2% to 3%.
  • The simulation with U.S. Treasuries shows a Pound/U.S. Dollar exchange rate at a median value of 1.2891 and a standard deviation of 0.0473 one year forward.
  • The same simulation is used to price short and long-dated foreign exchange options on the Pound versus the U.S. dollar at a strike price of 1.31.

 Author’s Note

 This simulation has been done jointly with a U.S. Treasury yield simulation in a way that reflects the correlation among the 12 factors driving yields in each country. For more on the companion U.S. Treasury simulation, please contact the author. Both the Gilt and the U.S. Treasury yield simulations impact foreign exchange rates, resulting in the following distribution of the Pound/U.S. dollar exchange rate one year forward:

Pricing for short- and long-dated European and American options to buy Pounds versus U.S. dollars at a strike price of 1.31 for quarterly maturities out to 20 years is shown below. Note that the data for American options is the lower bound on the fair-value price.

This Week’s Simulation of Gilt Yields

As explained in Prof. Robert Jarrow’s book cited below, forward rates contain a risk premium above and beyond the market’s expectations for the 3-month forward rate. We document the size of that risk premium in the graph below, which shows the zero-coupon yield curve implied by current United Kingdom Gilt prices compared with the annualized compounded yield on 3-month bills that market participants would expect based on the daily movement of government bond yields in 14 countries since 1962. The risk premium, the reward for a long-term investment, is positive and remains so over the full maturity range to 20 years. The graph also shows a steady downward shift in yields in the first three years, as explained below, followed by a gradual rise.

For more on this topic, see the analysis of government bond yields in 14 countries through September 30, 2024 given in the appendix.

Inverted Yields, Negative Rates, and United Kingdom Gilt Probabilities 10 Years Forward

In this week’s United Kingdom forecast, the focus is on three elements of interest rate behavior: the future probability of the recession-predicting inverted yield curve, the probability of negative rates, and the probability distribution of United Kingdom Gilt yields over the next decade.

We start from the closing United Kingdom Gilt yield curve published daily by the Bank of England and other information sources. Using a maximum smoothness forward rate approach, Friday’s implied forward rate curve shows 1-month rates at an initial peak of 4.82%. As maturities lengthen, there is some volatility until rates peak again at 5.77%. Forward rates then decline to a lower plateau of 4.15% at the end of the 20-year horizon.

Using the methodology outlined in the appendix, we simulate 100,000 future paths for the United Kingdom Gilt yield curve out to twenty years. The next three sections summarize our conclusions from that simulation.

Inverted Gilt Yields: Positive Spread Now, 26.4% Negative Spread Probability by April 11, 2025

A large number of economists have concluded that a downward sloping yield curve is an important indicator of future recessions. A recent example is this paper by Alex Domash and Lawrence H. Summers.

We measure the probability that the 10-year par coupon Gilt yield is lower than the 2- year par coupon Gilt for every scenario in each of the first 80 quarterly periods in the simulation.[1] The next graph shows that the probability of an inverted yield peaks at 26.4% in the 91-day quarterly period ending April 11, 2025.

Negative 3-Month Yields: 11.7% Probability by April 2, 2032

The next graph describes the probability of negative 3-month bill rates for all but the first 3 months of the next two decades. The probability of negative rates starts near zero but peaks at 16.1% in the period ending April 2, 2032.

Calculating the Default Risk from Interest Rate Maturity Mismatches

In light of the interest-rate-risk-driven failure of Silicon Valley Bank in the United States on March 10, 2023, we have added a table that applies equally well to banks, institutional investor, and individual investor mismatches from buying long-term United Kingdom Gilts with borrowed short-term funds. We assume that the sole asset is a 10-year United Kingdom Gilt purchased at time zero at par value of 100 Pounds. We analyze default risk for four different initial market values of equity to market value of asset ratios: 5%, 10%, 15%, and 20%. For the banking example, we assume that the only class of liabilities is deposits that can be withdrawn at par at any time. In the institutional and retail investor case, we assume that the liability is essentially a borrowing on margin/repurchase agreement with the possibility of margin calls. For all investors, the amount of liabilities (95, 90, 85 or 80) represents a “strike price” on a put option held by the liability holders. Failure occurs via a margin call, bank run, or regulatory takeover (in the banking case) when the value of assets falls below the value of liabilities.

The chart below shows the cumulative 10-year probabilities of failure for each of the 4 possible capital ratios when the asset’s maturity is 10 years. For the 5 percent case, that default probability is 44.50%.

This default probability analysis is updated weekly based on the United Kingdom Gilt yield simulation described in the next section. The calculation process is the same for any portfolio of assets with credit risk included.

 United Kingdom Gilt Yield Probabilities 10 Years Forward

In this section, the focus turns to the decade ahead. This week’s simulation shows that the most likely range for the 3-month bill yield in the Gilt market in ten years is from 0% to 1%, unchanged from last week. There is a 23.69% probability that the 3-month yield falls in this range. Note the downward shift in the second, third and fourth semi-annual periods. For the 10-year Gilt yield, the most likely range is from 2% to 3%. The probability of being in this range is 21.57%.

In a recent post on SeekingAlpha, we pointed out that a forecast of “heads” or “tails” in a coin flip leaves out critical information. What a sophisticated bettor needs to know is that, on average for a fair coin, the probability of heads is 50%. A forecast that the next coin flip will be “heads” is literally worth nothing to investors because the outcome is purely random.

The same is true for interest rates.

In this section we present the detailed probability distribution for both the 3-month bill rate and the 10-year Gilt yield 10 years forward using semi-annual time steps[2]. We present the probability of where rates will be at each time step in 1 percent “rate buckets.” The forecast for 3-month bill yields is shown in this graph:

3-Month Bill Yield Data:

SAS3monthGilt20241011 

The probability that the 3-month bill yield will be between 1% and 2% in 2 years is shown in column 4: 26.95%. The probability that the 3-month yield will be negative (as it has been often in Europe and Japan) in 2 years 2.43% plus 0.08% plus 0.00% plus 0.00% = 2.51% (difference due to rounding). Cells shaded in blue represent positive probabilities of occurring, but the probability has been rounded to the nearest 0.01%. The shading scheme works like this:

  • Dark blue: the probability is greater than 0% but less than 1%
  • Light blue: the probability is greater than or equal to 1% and less than 5%
  • Light yellow: the probability is greater than or equal to 5% and 10%
  • Medium yellow: the probability is greater than or equal to 10% and less than 20%
  • Orange: the probability is greater than or equal to 20% and less than 25%
  • Red: the probability is greater than 25%

The chart below shows the same probabilities for the 10-year Gilt yield derived as part of the same simulation.

10-Year United Kingdom Gilt Yield Data:

SAS10yearGilt20241011 

Modeling International Yield and Foreign Exchange Rate Correlation

The simulation of the United Kingdom Gilt yield curve and the Pound exchange rate is done simultaneously with simulations of risk-free government yield curves in multiple countries. This simulation is based in daily historical data from 1962 (U.S.), 1974 (Japan), 1979 (United Kingdom), 1997 (Germany) and six other countries. The forward-looking correlation between Gilt and U.S. Treasury 10-year zero coupon yields one year forward is given here:

Appendix: Gilt Yield Simulation Methodology

The probabilities are derived using the same methodology that SAS Institute Inc. recommends to its KRIS® and Kamakura Risk Manager® clients. A moderately technical explanation is given later in the appendix, but we summarize it briefly first.

Step 1: We take the closing Gilt yield curve as our starting point.

Step 2: We use the number of points on the yield curve that best explains historical yield curve shifts. We note in the following graph that Gilt yields span (by rate level and maturity) 75.28% of the historical experience in 14 countries:

For the highest degree of realism in a forward-looking simulation, using the international database is essential. Using daily government bond yield data from 14 countries from 1962 through September 30, 2024, we conclude that 12 “factors” drive almost all movements of government bond yields. The countries on which the analysis is based are Australia, Canada, France, Germany, Italy, Japan, New Zealand. Russia, Singapore, Spain, Sweden, Thailand, the United Kingdom, and the United States of America. No data from Russia is included after January 2022.

Step 3: We measure the volatility of changes in those factors and how volatility has changed over the same period.

Step 4: Using those measured volatilities, we generate 100,000 random shocks at each step and derive the resulting yield curve.

Step 5: We “validate” the model to make sure that the simulation EXACTLY prices the starting Gilt curve and that it fits history as well as possible. The methodology for doing this is described below.

Step 6: We take all 100,000 simulated yield curves and calculate the probabilities that yields fall in each of the 1% “buckets” displayed in the graph.

Do Nominal Yields Accurately Reflect Expected Future Inflation?

We showed in a recent post on SeekingAlpha that, on average, investors have almost always done better by buying long term bonds than by rolling over short term Treasury bills in the United States. That means that market participants have generally (but not always) been accurate in forecasting future inflation and adding a risk premium to that forecast. This study is being updated using the 14-country data set in the coming weeks.

Technical Details

Daily government bond yields from the 14 countries listed above form the base historical data for fitting the number of yield curve factors and their volatility. The Gilt historical data is provided by the Bank of England. The use of the international bond data increases the number of observations to more than 107,000 and provides a more complete range of experience with both high rates and negative rates than a Gilt data set alone provides.

The modeling process was published in a very important paper by David Heath, Robert Jarrow and Andrew Morton in 1992:

Professor Jarrow’s biography is available here.

The no-arbitrage foreign exchange rate simulation is based on this well-known paper by Amin and Jarrow:

For technically inclined readers, we recommend Prof. Jarrow’s book Modeling Fixed Income Securities and Interest Rate Options for those who want to know exactly how the “HJM” model construction works.

The number of factors, 12 for the 14-country model, has been stable since June 30, 2017.

Footnotes:

[1] After the first 20 years in the simulation, the 10-year yield cannot be derived from the initial 30-year term structure of yields.

[2] The actual simulation uses 91-day time steps and spans a 30-year time horizon.

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