Yield curve

Relationship to the business cycle

The slope of the yield curve is one of the most powerful predictors of future economic growth, inflation, and recessions. One measure of the yield curve slope (i.e. the difference between 10-year Treasury bond rate and the 3-month Treasury bond rate) is included in the Financial Stress Index published by the St. Louis Fed. A different measure of the slope (i.e. the difference between 10-year Treasury bond rates and the federal funds rate) is incorporated into the Index of Leading Economic Indicators published by The Conference Board.

An inverted yield curve is often a harbinger of recession. A positively sloped yield curve is often a harbinger of inflationary growth. Work by Arturo Estrella and Tobias Adrian has established the predictive power of an inverted yield curve to signal a recession. Their models show that when the difference between short-term interest rates (they use 3-month T-bills) and long-term interest rates (10-year Treasury bonds) at the end of a federal reserve tightening cycle is negative or less than 93 basis points positive, a rise in unemployment usually occurs. The New York Fed publishes a monthly recession probability prediction derived from the yield curve and based on Estrella's work.

All the recessions in the US since 1970 have been preceded by an inverted yield curve (10-year vs 3-month). Over the same time frame, every occurrence of an inverted yield curve has been followed by recession as declared by the NBER business cycle dating committee. The yield curve became inverted in the first half of 2019, for the first time since 2007.

Estrella and others have postulated that the yield curve affects the business cycle via the balance sheet of banks (or bank-like financial institutions). When the yield curve is inverted, banks are often caught paying more on short-term deposits (or other forms of short-term wholesale funding) than they are making on new long-term loans leading to a loss of profitability and reluctance to lend resulting in a credit crunch. When the yield curve is upward sloping, banks can profitably take-in short-term deposits and make new long-term loans so they are eager to supply credit to borrowers. This eventually leads to a credit bubble.

Theory

There are three main economic theories attempting to explain how yields vary with maturity. Two of the theories are extreme positions, while the third attempts to find a middle ground between the former two.

Market expectations (pure expectations) hypothesis

This hypothesis assumes that the various maturities are perfect substitutes and suggests that the shape of the yield curve depends on market participants' expectations of future interest rates. It assumes that market forces will cause the interest rates on various terms of bonds to be such that the expected final value of a sequence of short-term investments will equal the known final value of a single long-term investment. If this did not hold, the theory assumes that investors would quickly demand more of the current short-term or long-term bonds (whichever gives the higher expected long-term yield), and this would drive down the return on current bonds of that term and drive up the yield on current bonds of the other term, so as to quickly make the assumed equality of expected returns of the two investment approaches hold.

Using this, futures rates, along with the assumption that arbitrage opportunities will be minimal in future markets, and that futures rates are unbiased estimates of forthcoming spot rates, provide enough information to construct a complete expected yield curve. For example, if investors have an expectation of what 1-year interest rates will be next year, the current 2-year interest rate can be calculated as the compounding of this year's 1-year interest rate by next year's expected 1-year interest rate. More generally, returns (1+ yield) on a long-term instrument are assumed to equal the geometric mean of the expected returns on a series of short-term instruments:

${\displaystyle (1+i_{lt}{)}^n=(1+i_{st}^{\text{year }1})(1+i_{st}^{\text{year }2})\cdots (1+i_{st}^{\text{year }n}),}$

where i_st and i_lt are the expected short-term and actual long-term interest rates (but ${\displaystyle i_{st}^{\text{year}1}}$ is the actual observed short-term rate for the first year).

This theory is consistent with the observation that yields usually move together. However, it fails to explain the persistence in the shape of the yield curve.

Shortcomings of expectations theory include that it neglects the interest rate risk inherent in investing in bonds.

Liquidity premium theory

The liquidity premium theory is an offshoot of the pure expectations theory. The liquidity premium theory asserts that long-term interest rates not only reflect investors' assumptions about future interest rates but also include a premium for holding long-term bonds (investors prefer short-term bonds to long-term bonds), called the term premium or the liquidity premium. This premium compensates investors for the added risk of having their money tied up for a longer period, including the greater price uncertainty. Because of the term premium, long-term bond yields tend to be higher than short-term yields and the yield curve slopes upward. Long-term yields are also higher not just because of the liquidity premium, but also because of the risk premium added by the risk of default from holding a security over the long term. The market expectations hypothesis is combined with the liquidity premium theory:

${\displaystyle (1+i_{lt}{)}^n=r{p}_n+((1+i_{st}^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}1})(1+i_{st}^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}2})\cdots (1+i_{st}^{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}n})),}$

where ${\displaystyle r{p}_n}$ is the risk premium associated with an ${\displaystyle n}$ year bond.

Preferred habitat theory

The preferred habitat theory is a variant of the liquidity premium theory, and states that in addition to interest rate expectations, investors have distinct investment horizons and require a meaningful premium to buy bonds with maturities outside their "preferred" maturity, or habitat. Proponents of this theory believe that short-term investors are more prevalent in the fixed-income market, and therefore longer-term rates tend to be higher than short-term rates, for the most part, but short-term rates can be higher than long-term rates occasionally. This theory is consistent with both the persistence of the normal yield curve shape and the tendency of the yield curve to shift up and down while retaining its shape.

Market segmentation theory

This theory is also called the segmented market hypothesis. In this theory, financial instruments of different terms are not substitutable. As a result, the supply and demand in the markets for short-term and long-term instruments is determined largely independently. Prospective investors decide in advance whether they need short-term or long-term instruments. If investors prefer their portfolio to be liquid, they will prefer short-term instruments to long-term instruments. Therefore, the market for short-term instruments will receive a higher demand. Higher demand for the instrument implies higher prices and lower yield. This explains the stylized fact that short-term yields are usually lower than long-term yields. This theory explains the predominance of the normal yield curve shape. However, because the supply and demand of the two markets are independent, this theory fails to explain the observed fact that yields tend to move together (i.e., upward and downward shifts in the curve).

Historical development of yield curve theory

On August 15, 1971, U.S. President Richard Nixon announced that the U.S. dollar would no longer be based on the gold standard, thereby ending the Bretton Woods system and initiating the era of floating exchange rates.

Floating exchange rates made life more complicated for bond traders, including those at Salomon Brothers in New York City. Encouraged by Marty Liebowitz, traders began thinking about bond yields in new ways by the middle of the 1970s. Rather than think of each maturity (a ten-year bond, a five-year, etc.) as a separate marketplace, they began drawing a curve through all their yields. The bit nearest the present time became known as the short end—yields of bonds further out became, naturally, the long end.

Academics had to play catch up with practitioners in this matter. One important theoretic development came from a Czech mathematician, Oldrich Vasicek, who argued in a 1977 paper that bond prices all along the curve are driven by the short end (under risk-neutral equivalent martingale measure) and accordingly by short-term interest rates. The mathematical model for Vasicek's work was given by an Ornstein–Uhlenbeck process, but has since been discredited because the model predicts a positive probability that the short rate becomes negative and is inflexible in creating yield curves of different shapes. Vasicek's model has been superseded by many different models including the Hull–White model (which allows for time varying parameters in the Ornstein–Uhlenbeck process), the Cox–Ingersoll–Ross model, which is a modified Bessel process, and the Heath–Jarrow–Morton framework. There are also many modifications to each of these models, but see the article on short-rate model. Another modern approach is the LIBOR market model, introduced by Brace, Gatarek and Musiela in 1997 and advanced by others later. In 1996, a group of derivatives traders led by Olivier Doria (then head of swaps at Deutsche Bank) and Michele Faissola, contributed to an extension of the swap yield curves in all the major European currencies. Until then the market would give prices until 15 years maturities. The team extended the maturity of European yield curves up to 50 years (for the lira, French franc, Deutsche mark, Danish krone and many other currencies including the ecu). This innovation was a major contribution towards the issuance of long dated zero-coupon bonds and the creation of long dated mortgages.

Construction of the full yield curve from market data

The usual representation of the yield curve is in terms of a function P, defined on all future times t, such that P(t) represents the value today of receiving one unit of currency t years in the future. If P is defined for all future t then - by "bootstrapping" - we can easily recover the yield (i.e. the annualized interest rate) for borrowing money for that period of time via the formula

${\displaystyle Y(t)=P(t{)}^{-1/t}-1.}$

The significant difficulty in defining a yield curve therefore is to determine the function P(t). P is called the discount factor function or the zero coupon bond.

Yield curves are built from either prices available in the bond market or the money market. Whilst the yield curves built from the bond market use prices only from a specific class of bonds (for instance bonds issued by the UK government) yield curves built from the money market use prices of "cash" from today's LIBOR rates, which determine the "short end" of the curve i.e. for t ≤ 3m, interest rate futures which determine the midsection of the curve (3m ≤ t ≤ 15m) and interest rate swaps which determine the "long end" (1y ≤ t ≤ 60y).

The example given in the table at the right is known as a LIBOR curve because it is constructed using either LIBOR rates or swap rates. A LIBOR curve is the most widely used interest rate curve as it represents the credit worth of private entities at about A+ rating, roughly the equivalent of commercial banks. If one substitutes the LIBOR and swap rates with government bond yields, one arrives at what is known as a government curve, usually considered the risk free interest rate curve for the underlying currency. The spread between the LIBOR (or swap) rate and the government bond yield of similar maturity is usually positive, meaning that private borrowing is at a premium above government borrowing. This spread is a measure of the difference in the risk tolerances of the lenders to the two types of borrowing. For the U. S. market, a common benchmark for such a spread is given by the so-called TED spread.

In either case the available market data provides a matrix A of cash flows, each row representing a particular financial instrument and each column representing a point in time. The (i,j)-th element of the matrix represents the amount that instrument i will pay out on day j. Let the vector F represent today's prices of the instrument (so that the i-th instrument has value F(i)), then by definition of our discount factor function P we should have that F = AP (this is a matrix multiplication). Actually, noise in the financial markets means it is not possible to find a P that solves this equation exactly, and our goal becomes to find a vector P such that

${\displaystyle AP=F+\epsilon }$

where ${\displaystyle \epsilon }$ is as small a vector as possible (where the size of a vector might be measured by taking its norm, for example).

Even if we can solve this equation, we will only have determined P(t) for those t which have a cash flow from one or more of the original instruments we are creating the curve from. Values for other t are typically determined using some sort of interpolation scheme.

Practitioners and researchers have suggested many ways of solving the A*P = F equation. It transpires that the most natural method – that of minimizing ${\displaystyle ϵ}$ by least squares regression – leads to unsatisfactory results. The large number of zeroes in the matrix A mean that function P turns out to be "bumpy".

In their comprehensive book on interest rate modelling James and Webber note that the following techniques have been suggested to solve the problem of finding P:

1. Approximation using Lagrange polynomials
2. Fitting using parameterised curves (such as splines, the Nelson-Siegel family, the Svensson family, the exponential polynomial family or the Cairns restricted-exponential family of curves). Van Deventer, Imai and Mesler summarize three different techniques for curve fitting that satisfy the maximum smoothness of either forward interest rates, zero coupon bond prices, or zero coupon bond yields
3. Local regression using kernels
4. Linear programming

In the money market practitioners might use different techniques to solve for different areas of the curve. For example, at the short end of the curve, where there are few cashflows, the first few elements of P may be found by bootstrapping from one to the next. At the long end, a regression technique with a cost function that values smoothness might be used.

The yield curve as parametric model: Nelson–Siegel–Svensson model

While bootstrapping provides an exact, non-parametric extraction of the yield curve, it can result in an unstable or jagged forward curve. To achieve a smoother, more economically intuitive term structure, analysts turn to parametric models. The Nelson–Siegel–Svensson (NSS) model is a widely used framework for fitting and interpolating yield curves from observed bond prices or rates. It extends the three-factor model of Nelson and Siegel (1987), which describes the yield curve using three components — level, slope, and curvature — each governed by an exponential decay term.

Svensson (1994) introduced a fourth factor and a second decay parameter, giving the model greater flexibility to capture humps or troughs at different maturities:

${\displaystyle y(\tau )={\beta }_0+{\beta }_1\frac{1-e^{-\tau /{\lambda }_1}}{\tau /{\lambda }_1}+{\beta }_2\left(\frac{1-e^{-\tau /{\lambda }_1}}{\tau /{\lambda }_1}-e^{-\tau /{\lambda }_1}\right)+{\beta }_3\left(\frac{1-e^{-\tau /{\lambda }_2}}{\tau /{\lambda }_2}-e^{-\tau /{\lambda }_2}\right)}$

where ${\displaystyle \tau }$ is maturity, ${\displaystyle {\beta }_0}$ controls the long-run level, ${\displaystyle {\beta }_1}$ the short-end slope, ${\displaystyle {\beta }_2}$ and ${\displaystyle {\beta }_3}$ medium-term curvature components, and ${\displaystyle {\lambda }_1}$, ${\displaystyle {\lambda }_2}$ the decay speeds of those components.

The NSS model is used operationally by several major central banks — including the European Central Bank, the Deutsche Bundesbank, and the Swiss National Bank — to publish official fitted yield curves. Its parameters admit direct economic interpretation: ${\displaystyle {\beta }_0}$ represents the long-run yield to which the curve converges, while ${\displaystyle {\beta }_1}$ captures the term spread between short and long rates. Diebold and Li (2006) demonstrated that re-labelling the Nelson–Siegel factors as level, slope, and curvature and treating them as latent dynamic factors yields strong out-of-sample forecasting performance.

Because estimation requires non-linear optimisation over six parameters, convergence is sensitive to initial values; the Bank for International Settlements has published a technical note addressing common pitfalls. Interactive tools that allow users to vary the NSS parameters and observe the resulting curve shape in real time have been developed to aid intuition.

Effect on bond prices

There is a time dimension to the analysis of bond values. A 10-year bond at purchase becomes a 9-year bond a year later, and the year after it becomes an 8-year bond, etc. Each year the bond moves incrementally closer to maturity, resulting in lower volatility and shorter duration and demanding a lower interest rate when the yield curve is rising. Since falling rates create increasing prices, the value of a bond initially will rise as the lower rates of the shorter maturity become its new market rate. Because a bond is always anchored by its final maturity, the price at some point must change direction and fall to par value at redemption.

A bond's market value at different times in its life can be calculated. When the yield curve is steep, the bond is predicted to have a large capital gain in the first years before falling in price later. When the yield curve is flat, the capital gain is predicted to be much less, and there is little variability in the bond's total returns over time.

As market rates of interest increase or decrease, the impact is rarely the same at each point along the yield curve, i.e. the curve rarely moves up or down in parallel. Because longer-term bonds have a larger duration, a rise in rates will cause a larger capital loss for them, than for short-term bonds. But almost always, the long maturity's rate will change much less, flattening the yield curve. The greater change in rates at the short end will offset to some extent the advantage provided by the shorter bond's lower duration.

Long duration bonds tend to be mean reverting, meaning that they readily gravitate to a long-run average. The middle of the curve (5–10 years) will see the greatest percentage gain in yields if there is anticipated inflation even if interest rates have not changed. The long-end does not move quite as much percentage-wise because of the mean reverting properties.

The yearly 'total return' from the bond is a) the sum of the coupon's yield plus b) the capital gain from the changing valuation as it slides down the yield curve and c) any capital gain or loss from changing interest rates at that point in the yield curve.