SQUARE VERSUS CONVEXITY

MEASURING TERM STRUCTURE SHIFTS

M- SQUARE VERSUS CONVEXITY

models against the possible nonstationarities in the stochastic processes for the term structure, results for these two periods are analyzed separately.

Table 4.1 reports the sum of absolute deviations of the M-absolute hedging strategy as a percentage of the sum of absolute deviations of the du- ration strategy, for the two separate time periods. The M-absolute strategy reduces the immunization risk inherent in the duration model by more than half in both time periods. This finding implies that the changes in the height of the term structure of instantaneous forward rates must be accompanied by significant changes in the slope, curvature, and other higher order term structure shape parameters.

M-Square versus Convexity 91

The M-square risk measure is defined as the weighted average of the squared differences between cash flow maturities and the planning horizon, where weights are the present values of the bond’s (or a bond portfolio’s) cash flows, given as proportions of the bond’s (or the bond portfolio’s) price:

Unlike convexity, the M-square measure is specific to a given planning hori- zon. The M-square model selects the bond portfolio that minimizes the M- square of the bond portfolio, subject to the duration constraint (i.e., Duration=Planning horizon). For the special case, when planning horizon is equal to zero, the M-square converges to the convexity of the bond. To get more insight regarding the M-square risk measure, consider the following inequality.⁶

where V_H is the target future value of the bond portfolio at the plan- ning horizon H.

Unlike the M-absolute model, the M-square model is based upon two risk measures. If the portfolio is immunized with respect to duration (i.e., D=H), then equation 4.23 puts a lower bound on the target future value, which is a function of a constant K₄and the portfolio’s M-square. The term K₄depends on term structure movements and gives the maximum slope of the shift in the term structure of instantaneous forward rates across the ma- turity term t.⁷Mathematically, K₄can be defined as follows:

for all tsuch that, 0≤t≤t_N.

The term K₄is outside of the control of a portfolio manager. A portfo- lio manager can control the portfolio’s M-square, however, by selecting a particular duration-immunized bond portfolio. The smaller the magnitude of M-square, the lower the risk exposure of the bond portfolio. A portfolio that has cash flows centered closer to its planning horizon has a lower M- square. However, only a zero-coupon bond maturing at horizon H has a zero M-square, which implies that only this bond is completely immune (4.24) K₄≥ ∂[∆f t( )] /∂t

(4.23)

∆V ∆

V^H D H f H K M

H

≥ − −( )× ( )−1 × 2 ⁴

2

(4.22)

M t H w_t

t t t tN

2 2

1

= − ×

=

∑

= ^{( )}

from interest rate risk. An implicit condition required for the inequality (4.23) to hold is that the bond portfolio does not contain any short posi- tions (see Appendix 4.1). The immunization objective of the M-square model is to select a bond portfolio that minimizes the portfolio’s M-square, subject to the duration constraint D=H.

A linear relationship exists between M-square and convexity, given as follows:

If duration is kept constant, then M-square is an increasing function of con- vexity. Equation 4.25 leads to the well-known convexity-M-square para- dox.⁸As shown in equation 3.18 in the previous chapter, higher convexity is beneficial since it leads to higher returns. On the other hand, equation 4.23 suggests that M-square should be minimized, in order to minimize immu- nization risk. These two statements contradict each other since increasing convexity is equivalent to increasing M-square (see equation 4.25). This convexity-M-square paradox can be resolved by noting that the “convexity view” assumes parallel term structure shifts, while the “M-square view” as- sumes nonparallel term structure shifts. Obviously, perfect parallel shifts in the term structure are not possible. Hence, which view is valid, depends on the extent of the violation of the parallel term structure shift assumption (i.e., Is this violation slight or significant?).

Before we address this issue using empirical data, note that the convex- ity view is not consistent with bond market equilibrium, because a riskless positive return with zero initial investment can be guaranteed by taking a long position in a barbell or high positive-convexity portfolio along with a simultaneous and equal short position in a bullet or low-convexity portfo- lio, each portfolio having equal durations. Hence, to avoid riskless arbitrage opportunities, bond convexity must be priced in equilibrium. Specifically, if convexity is desirable, the price of the barbell portfolio will be bid up much like the expected bid-up in the price of a stock portfolio with positive skew- ness. This conclusion is at odds with the convexity view that bond portfolio returns can be increased through higher portfolio convexity.

However, since the M-square view is based on risk minimization condi- tions against nonparallel shifts in the term structure of interest rates, this view is consistent with equilibrium conditions, as it requires no specific as- sumptions regarding the shape of these shifts.

In the following subsection, we provide a unified framework, which al- lows both the convexity view and the M-square view as special cases of the general framework. Then we show the empirical relationship between (4.25) M²=CON− × ×2 D H+H²

M-Square versus Convexity 93

bond convexity (which is linear in M-square) and ex ante bond returns. We also investigate whether higher convexity portfolios lead to higher immu- nization risk.

Resolving the Convexity/M-Square Paradox

Unlike the lower bound approach to the M-square model given earlier, Fong and Fabozzi (1985) and Lacey and Nawalkha (1993) suggest an alternative two-term Taylor-series-expansion approach to the M-square model. This approach leads to a generalized framework for resolving the convexity/

M-square paradox.

Consider a bond portfolio at time t=0 that offers the amount C_t at time t=t₁, t₂, . . . , t_N.The return R(H) on this portfolio between t =0 and t =H(an investment horizon) can be given as:

where as shown in equation 4.17, V_H' is the realized future value of the port- folio at the planning horizon H after the term structure of forward rates shifts to f'(t), and V₀is the current value of the portfolio using the current term structure of forward rates f(t). As shown in Appendix 4.2, using a two- term Taylor series expansion, equation 4.26 can be simplified as:

where, R_F(H) is the riskless-return on any default-free zero-coupon bond with maturity Hgiven as:

and εis the error term due to higher order Taylor series terms.

The duration coefficient in equation 4.28 is defined as follows:

and the M-square coefficient in equation 4.27 is defined as a difference of two effects as follows:

(4.29) γ1= −∆f H( ) (× +1 R H_F( ))

(4.28) R H_F

( )

^=exp_

∫

^0Hf s ds

( )

^_⁻¹

(4.27) R H

( )

⁼R H_F

( )

⁺γ1^×D H⁻  + ×γ2 M +ε

2

(4.26)

R H V V

V

( )

^{= } H^{' − 0}

0

where

and

The convexity effect (CE) is positive for any term structure shift such that an increase in convexity (i.e., same as increase in M-square) enhances return regardless of the direction of the shift. This demonstrates the tradi- tional view of convexity. The risk effect (RE) can be either positive or nega- tive, depending on whether the instantaneous forward rate at the planning horizon Hexperiences a positive or a negative slope shift. A positive slope shift will decrease the value of the M-square coefficient (see equation 4.30) such that a higher-M-square portfolio (i.e., the same as a higher convexity portfolio) will result in a decline in portfolio return. A negative slope shift will increase the value of the M-square coefficient such that a higher-M- square portfolio (i.e., the same as a higher convexity portfolio) will result in an increase in portfolio return.

The convexity view assumes an insignificant risk effect (i.e., parallel shifts) such that only the convexity effect matters. Within this view, higher convexity always leads to higher return, which is inconsistent with the equi- librium conditions as outlined in Ingersoll, Skelton, and Weil (1978). Con- versely, the equilibrium-consistent M-square view assumes an insignificant convexity effect such that only the risk effect matters. Within this view, the desirability of convexity depends on whether the risk effect is positive, neg- ative, or insignificantly different from zero.

Example 4.5 Reconsider the $1,000 face value, five-year, 10 percent an- nual coupon bond that was priced in Example 4.3. In this example, the ini- tial term structure of instantaneous forward rates is given as:

f t

( )

^{=0 06 0 02. + . × −}t ^{0 003. × +}t^{2 0 0004. ×}t³

(4.32) RE=Risk Effect=

(

+

( ) )

^∂

⁽

_∂

⁾







 1

2 1 R H f t

F t

∆ ( )

t H=

(4.31) CE=Convexity Effect= ¹₂

(

¹+^{R HF}

( ) ) (

^{∆f H}

^{( )} )

²

(4.30) γ2 =CE RE−

M-Square versus Convexity 95

The instantaneous shift in the forward rates and the new term structure of forward rates were given in that example as:

and

Consider the instantaneous return on this bond (atH=0) using equation 4.27. SubstitutingH=0, equation 4.27 simplifies to the following equation:

where Dis the duration, and CONis the convexity of the bond.⁹The con- vexity effect and the risk effect are given as follows:

The M-square (or convexity) coefficient is equal to CE−RE= 0.0020125. The risk effect completely dominates the convexity effect in this example. This could be because in this example, the slope change is high, equal to −0.004 or negative 40 basis points per year. However, note that even if slope change were only 1 basis point per year, the risk effect would still be equal to ¹⁄2(0.0001)=0.00005, which is four times the con- vexity effect of 0.0000125 produced by 50 basis points shift in the height of the term structure. What this suggests is that even very small changes in the slope (or curvature, etc.) of the term structure of forward rates can vi- olate the assumption of parallel term structure shifts sufficiently, such that the risk effect dominates the convexity effect. This is also consistent with a number of empirical studies that show that convexity adds risk but not extra return to option-free bond portfolios.

CE=Convexity Effect=¹₂

(

^∆f

( )

⁰

)

²⁼¹₂

⁽

^{0 005.}

⁾

^2=0..

( )

0000125 1

RE=Risk Effect=2 ∂

( )

∂











∆f t

t  = −  = −

= t 0

1

2 0 004. 0 002.

R V V

V D f CON CE RE

0 ^{0 0} 0

0

( )

^{= '− = − ×∆ ( )+ ×[ − ]+ε}

f t'( )=0 065 0 016. + . × −t 0 003. × +t² 0 0004. ×t³

∆f t( )=0 005 0 004. − . ×t

From Example 4.3, the instantaneous return on the bond can be given as:

Approximating this return using the two-term Taylor series expansion given earlier we get:

where

and

Substituting duration and convexity in the Taylor series expansion, we get:

or

The approximation of 1.771 percent is extremely close to the actual re- turn of 1.769 percent (the difference equals 0.002 percent).

Note that the approximation is good because it considers the risk effect consistent with the M-square view. Suppose, we assumed perfect parallel shifts consistent with the convexity view, instead. Then the risk effect would be assumed to be zero, and the approximation would be given as follows:

R

( )

0 ^≅1 771^{. %}

R V V

0 ⁰V ⁰ 4 146 0 005 19 1 0 0020125

( )

^{= '−}0 ^{= − . × . + . × . +ε}

CON= e e e

× + × + ×

1² 100 2 100 3 100

0 0691 2

0 1536 2

0 25

. . . 111

2 0 3616

2 0 4875

2 0 4

4 100 5 100 5 1000

+ × + × + ×

e^. e^. e^{. 8875}

1002 11 19 1

$ . = .

D= e e e

× + × + × + ×

1 100 2 100 3 100 4

0 0691. 0 1536. 0 2511.

1

100 5 100 5 1000 1002

0 3616 0 4875 0 4875

e^. e^. e^.

.

+ × + ×

1

11 =4 146.

R V V

V D CON

0 ^{0 0} 0 005 0 0020125

0

( )

^{= '− = − × . + × . +ε}

R V V

0 V 1019 84 1002 11

1002 11

17 73 1

0 0

0

( )

^{= '− = . −}_. ^{. =} _{0002 11}^._. ^{=1 769. %}

M-Square versus Convexity 97

or

The return of −2.049 percent is consistent with the convexity view out- lined in the previous chapter, and is very different from the actual return of 1.769 percent (the difference equals −3.818 percent).

Convexity, M-Square, and Ex-Ante Returns

Lacey and Nawalkha (1993) test a modified version of equation 4.27 in order to empirically distinguish between the risk effect (caused by slope changes) and the convexity effect (caused by second-order effect of height changes). The equation tested is obtained from equation 4.27 by substitut- ing the linear relationship between M-square and convexity given in equa- tion 4.25 as follows:

where

An ex-ante version of equation 4.33 is tested with CRSP government bond data using pooled cross-sectional time-series regressions of two- month excess holding period returns of U.S. Treasury bonds on their dura- tion and convexity measures over the period January 1976 through November 1987. The results of these tests over the whole sample period as well as over selected five-year periods are reported in Table 4.2.

The convexity coefficient (same as the M-square coefficient in equation 4.27) is negative in all eight subperiods, and is negative and statistically sig- nificant over two of these periods. In general, high positive convexity is not (4.34) β₀= − ×γ₁ H+γ₂×H² and β₁=γ₁−2γ₂×H

(4.33) R H( )−R H_F( )=β0+β1× +D γ2×CON+ε

R

( )

0 ^{≅ −}2 049^{. %}

R V V

0 ⁰V ⁰ 4 146 0 005 19 1 0 0000125

0

( )

^{= '− = − . × . + . × . +ε}

associated with positive excess returns, a conclusion that rejects the “con- vexity view,” consistent with the theoretical criticisms of bond convexity given by Ingersoll, Skelton, and Weil (1978), among others.

However, the statistically significant negative values of the convexity co- efficient over two subperiods provide some evidence that convexity is priced, and that increasing the level of positive convexity reducesthe ex- ante excess return on a bond portfolio. The negative relationship between the convexity exposure and excess holding-period bond returns is not in- consistent with the results of Fama (1984), which imply a positive slope, but a negative curvaturefor the term structure of excess holding-period returns.

Convexity, M-Square, and Immunization Risk

The previous section demonstrated that increasing convexity (or M-square) does not increase ex-antereturns on bonds. This section shows that holding duration constant, and increasing the absolute size of convexity (or M- square) leads to higher immunization risk for bond portfolios. Bond port- folios are constructed (using CRSP bond data) to have different levels of convexity exposure, while keeping the duration equal to two months (i.e., 0.16667 years). The standard deviation of the excess returns over two- month holding periods is measured for each of these portfolios. The excess TABLE 4.2 Ex Ante Bond Returns and Convexity Exposure

Number of

Test Period β₁ γ₂ Observations

Jan. 1976−Nov. 1987 0.00039 −0.000026 3,881

Jan. 1976−Dec. 1981 −0.00083 −0.000048 1,553

Jan. 1982−Nov. 1987 0.00126^a −0.000034 2,328

Jan. 1977−Dec. 1982 0.00029 −0.000043 1,682

Jan. 1978−Dec. 1983 −0.00004 −0.000057 1,808

Jan. 1979−Dec. 1984 0.00057 −0.000075^c 1,942

Jan. 1980−Dec. 1985 0.00109^c −0.000071^c 2,097

Jan. 1981−Dec. 1986 0.00120^b −0.000041 2,263

aIndicates significant at the 0.001 level.

bIndicates significant at the 0.05 level.

cIndicates significant at the 0.10 level.

Note: The number of observations in each period test is determined by (1) the num- ber of two-month holding periods in the particular test period, and (2) the number of bonds within each two-month holding period.

Closed-Form Solutions for M-Square and M-Absolute 99

returns are defined as the portfolio’s duration-immunized return less the riskless return.

Table 4.3 reports the standard deviation of the excess holding period re- turns for portfolios with different levels of convexity exposures, for the full sample period and the two subperiods, 1976 through 1981 and 1982 through 1987. Figure 4.1 illustrates these standard deviations graphically.

A clear relationship is shown between portfolio convexity and immu- nization risk, where risk is defined by the standard deviation of the portfolio’s excess return. High-convexity portfolios (both positive and negative) have the highest risk, while low-convexity portfolios (both positive and negative) have the lowest risk. Although the degree of tilt between convexity and risk is not constant through time—the tilt is steeper over 1976 through 1981 than it is over 1982 through 1987—a monotonic relationship between convexity and risk holds for each period under examination. Thus, these results demon- strate that the magnitude of convexity exposure increases immunization risk.

CLOSED-FORM SOLUTIONS FOR M-SQUARE AND