COMPUTING MAXIMUM SMOOTHNESS FORWARD RATE CURVES

COMPUTING MAXIMUM SMOOTHNESS

FORWARD RATE CURVES

October 2000

KianGuan Lim

*

_{and Qin Xiao}

**

* _{Director of The National University of Singapore Center for Financial Engineering, Singapore.} ** _{Quant Scientist, Goldman Sachs Energy Modeling, UK.}

COMPUTING MAXIMUM SMOOTHNESS

FORWARD RATE CURVES

1. INTRODUCTION

Adams and Deventer (1994) presents a new approach1 _{to yield curve smoothing that}

provides a sound basis for implementing many of the no-arbitrage term structure models such as

Vasicek (1977), Heath, Jarrow and Morton (1992), Hull and White (1993) and so on. By carefully

defining the criterion for the best fitting yield curve to have maximum smoothness for the

forward rate curve, they arrive at a simple but powerful method providing a closed-form

solution for a yield curve that fits all observed yields. A key result in that paper that enables this

method or procedure is that the smoothest forward rate curves are produced by a fourth-degree

polynomial with the cubic term missing. In this paper we show that excluding the cubic term is

incorrect and sup-optimal. We provide a correct proof of the result that the smoothest forward

rate curves are produced by an unconstrained fourth-degree polynomial. We also provide the

numerical algorithm to compute the corresponding yield and the forward rate curves.

2. MAXIMUM SMOOTHNESS FORWARD RATE CURVE

Let Pi be the price of a zero coupon bond with par value 1 and time to maturity ti. Suppose there are m such observations: P₁ , P₂ , …… , P_m . Let the instantaneous spot yield and

forward rates be y(t) and f(t) respectively. Then,

, i=1, 2,…, m. (1)

i t

P ds

s f

i

ln )

(

0

− =

∫

The relationship between the forward rates and the yield is thus

, i=1, 2,…, m. (2)

i i t

t t y ds s f

i

) ( )

(

0

Given the m observed Pi’s, we can find the smoothest forward rate curve such that the m constraints in (1) are satisfied. Then, by definition we would also have satisfied all the observed

yields as in (2). From the forward rate curve f(s), for 0 ≤ s ≤ tm , we can obtain via (2) the yield

curve. This yield curve should fit all the m observed yields.

A maximum smoothness forward rate curve is one that minimizes the total curvature of

the curve and that fits the observed yields exactly. Assuming f

( )

⋅ ∈C2

[

0,tm

]

, tm ≤ T, where f(.)

is of a polynomial form, the general criterion for the total curvature is the integral of the squared

second-order differential function

. (3)

ds s f Z =

∫

T ′′

0

2_{( )}

Maximum smoothness obtains when Z is minimized subject to (1) and to some auxiliary

conditions ensuring continuity of the curve and its derivatives. The estimation of the polynomial

function coefficients becomes an optimization problem

min T f s ds

f

∫

0 ′′ 2_{( )}

s.t.

∫

f( i , i=1, 2,…, m.

t

P ds

s

i

ln )

0

− =

0

) 0 ( r

f = , (4)

0 ) ( ' t_m =

f . (5)

Assumption (4) basically fixes the instantaneous spot rate at r0. Assumption (5) follows that in

Adams and Deventer (1994). It can be suitably generalized to f'(t_m)=r_m where rm is a

non-zero slope.

Integrating f(.), we obtain

. (6)

∫

t f s ds= f t t−

∫

tsf′ s ds

Since tsf (s)ds t f (t) tsf (s)ds ts f (s)ds,

0 0 0

2

2 _{′ − ′ − ′′}

= ′

∫

∫

∫

therefore

∫

tsf′ s ds= t f′ t −

∫

ts f ′′ s ds

0 0

2 2

2

1_{[ ( ) ( ) ]}

)

( . (7)

Substituting (7) into (6), we obtain

∫

t f s ds= f t t− t f′ t + s f ′′ s ds

0 0

2 2 1 2

2

1 _{( ) ( )}

) ( )

(

∫

t . (8)

Putting g(t)= f ′′(t), 0≤t ≤T ,

we have f′ t =

∫

tg s ds+ f′ = g s ds (9)

0 ( ) (0) 0 ( )

)

(

∫

t

and f t =

∫

t f′ s ds+r =

∫ ∫

t ug v dvdu+r . (10)

0 ( ) 0 0 0 ( ) 0

) (

Substituting (9) and (10) into (8), the constraint becomes

i

t t t

i s

i t g s ds s g s ds P

t r dvds v g

i i i

ln )

( )

( )

) ( (

0 0 0

2 2 1 2

2 1

0 + 0 − + =−

∫

∫

∫

∫

, for i=1,...,m (11)

We use the indicator function

.

  

< ≥ =

0 , 0

0 ,

1 ) (

t t t

u

The constraint is then expressed as

i t

i

i s u t s g s ds P

Q ( ) ( ) ln

0 2 2

1 _{− =−}

+

∫

,

for i=1,...,m (12)

where

i

t t

i i s

i i

i t u t s g v dvds t u t s g s ds rt

Q ₀

0 0

2 2 1

0 ( ) ( ) ( )

)

( − − − +

=

_∫

_∫

_∫

. (13)

Equations in (12) are identical to equations in (A7) in Adams and Deventer (1994). However,

Let λi for i be the Lagrange multipliers corresponding to the constraints in

(13). The objective function becomes

m ,..., 1 =

∫

+ = T

g iZ g 0 g s ds

2 (.), [ , ] ( ) min λ λ +    + − −

∫

g v dv t u t s g s s g s ds

t_i s _{i i}

0 2 2 1 2 2

1 _{( ) ( ) ( )]}

) ( )[ ) i P    ₋

∫ ∑

= s t u T m i i i 0 1 ( λ ln ( ₀ 1 i m i i t r + = λ

∑

. (14)

Suppose g* _{is the solution of the optimization problem, then}

0 ] [ 0 * _{+ =} = ε ε

ε Z g h

d d

for any continuous function h(.) defined on [0,T] such that h(s)=g(s)-g*_{(s), where g}*_{(s) is}

optimal. From (14) we have

0 ] [ 0 * _{+ =} = ε ε

ε Z g h

d d ds s h t s dv v h t s t u s h s g T m i s i i i i

∫

_ +

∑

−

∫

+ − _ = 0 1 0 2 2 2 1 *_{( ) ( ) ( )[ ( ) ( ) ( )]} 2 λ = ds s h t s s t u s g T m i i i

i ( ) ( ) ( )

) ( 2 0 1 2 2 2 1 * _⋅       _{+ − −}

∫

∑

₌ λ

=

+

∫ ∑

∫

= 0. (15)

= − T m i s i i

iu t s t h v dv ds

0 1 0 ] ) ( ][ ) ( [ λ

Lemma

Given A(.) and h(.) are continuous functions and B(.) is integrable, then

∫

b +

∫

⋅

∫

⋅ = (L1)

a

b a

u

a h v dv du

u B du u h u

A( ) ( ) ( ) [ ( ) ] 0

if and only if

for all a ≤ u ≤ b. (L2)

∫

− = b

u B v dv

u

A( ) ( )

Proof

(L1) can be written as

∫

b +

∫

∫

a

b a

b

u B v dv du

u h dv v h v

A( ) ( ) ( )[ ( ) ]

=

∫

b +

∫

∫

a

b a

b

v B u du dv

v h dv v h v

A( ) ( ) ( )[ ( ) ]

∫

+

∫

=

= b

a

b

v B u du dv

v A v

h( )[ ( ) ( ) ] 0

For any continuous h(.), therefore (L1) obtains if and only if (L2) holds.

Q.E.D.

Applying the above lemma, (15) equals to zero if and only if

∑

₌ − − =−

∫ ∑

₌ −

+ m

i

T s

m

i i i i i

i

iu t s s t u t v tdv

s g

1 1

2 2 2 1

*_{( ) λ ( ) ( ) λ ( )}

2 (16)

for all 0≤s≤T. Thus

∑

+ =

− −

= m

i j

j j t t

t g

1

2 4

1

*_{( ) λ ( ) when}

1

+

≤ < _i

i t t

t , i=0,...,m−1.

Thus _g*(_t) is a continuous function in second-order polynomial form in each interval [ _{, ]}.

1

+

k k t

t

By integrating g*_{(t), we can obtain}

when t

i i i i

it bt ct d t e

a t

f_{( )}= 4 + 3 + 2 + +

i

The maximum smoothness forward curve f(.) is thus an unconstrained fourth-order polynomial

function in each segment and is second-order continuously differentiable. This is summarized in

the following proposition.

Proposition

When f(0)=r₀ is known and f′(t_m)=0, the function of the maximum smoothness forward

curve is a fourth-order polynomial spline and is second-order continuously differentiable in the

range (0,T).

Proposition 1 shows that it is sub-optimal to omit the cubic term as in Adams and Deventer

(1994). They had incorrectly ignored the fact that Qi is a function of g(s) and thus omitted some terms in the differentiation step as in (15). Indeed it is easy to see that if we constrain bi = ci = 0

in (17), we arrive exactly at the solution (A14) in their paper. Clearly, avoiding these constraints

would produce a superior optimal solution, i.e. larger smoothness in our case.

3. ALGORITHM

In this section we show how to implement the optimization solution to obtain the

maximum smoothness forward rate curve and also the corresponding yield curve. The program

can be transformed to a quadratic form. We then obtain an explicit solution by the

Lagrange-multiplier method. From the forward rate function in (17), by integrating

t f t dt at bt c_i dt

t

t

t i i

i i

i i

2 2

2_{( ) (12 6 2 )}

1 1

∫

−

∫

−

+ + =

′′

= 5 2 ₃₆ 4 ₁₂ 3 2 ₁₆ 3 ₁₂ 2 ₄ 1 2

i i i i i i

i i i

i i

i i i

i + ∆ ab + ∆ b + ∆ ac + ∆ bc + ∆ c

5 144∆ _a

where ,                 = i i i i i e d c b a i x                 ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ = 0 0 0 0 0 0 0 0 0 0 0 0 4 6 8 0 0 6 12 18 0 0 8 18 1 2 3 2 3 4 3 4 5 5 144 i i i i i i i i i i h ,

∆ l , .

i l i l

i =t −t−1 l=1,...,5

Then the object function becomes

minxThx

x

where ,

          = m 1 x x x _M           = m 1 h 0 0 h h _O

The constraints are as follows.

(a) Fitting the observed points:

      − ∆ + ∆ + ∆ + ∆ + ∆ −1 1 2 2 1 3 3 1 4 4 1 5 5 1 _ln i i i i i i i i i i i i P P e d c b

a , i=1,...,m (18)

(b) Continuity of the spline function at the joints:

0 ) ( ) ( ) ( ) ( )

( 3 ₁ 2 _{1 1}

1 4

1 − + + − + + − + + − + + − =

+ i i i i i i i i i i i i i

i a t b b t c c t d d t e e

a , (19) 1 ,..., 1 − = m i

(c) Continuity of the first-order differential of the spline function

0 ) ( ) ( 2 ) ( 3 ) (

4 2 _{1 1}

1 3

1 − + + − + + − + + − =

+ i i i i i i i i i i

i a t a a t c c t d d

a , i=1,...,m−1 (20)

(d) Continuity of the second-order differential of the spline function

0 ) ( 2 ) ( 6 ) (

12 2 _{1 1}

1 − + + − + + − =

+ i i i i i i i

i a t b b t c c

a i=1,...,m−1 (21)

and

0

) 0 ( r

f = ; e1=r0 (22)

0 ) ( = ′ t_m

f ; d₁=0 (23)

All the constraints (18) to (23) are linear with respect to . We can write the constraints in

matrix form , where A is a 4m-1 × 5m matrix, and B is a 4m-1 × 1 vector. Let

be the corresponding Lagrange multiplier vector to the constraints. The

object function becomes

x

B Ax=

T m ]

...,λ_{4 −1} ,

[ _{1 2}

= λ λ λ

min ( , ) ( )

, x x hx Ax B

T T

xλ Z λ = +λ −

If [x0 , λ0] is the solution, we should have

0 2

) (

0 0,

= +

= ∂

∂

λ

0 T 0 A λ

hx

λ

x,

xZ _x

0 )

(

0 0,

= − = ∂

∂

λ

B Ax

λ

x,

λZ _x 0

or _.

     =          

 

B 0

λ

x 0 A

A 2h

0 0 T

then _

     ⋅    

  ⋅ =

−

− × ×

B 0 0

A A 2h ] 0

I [ x

1 T

0 5m5m 5m (4m1)

The explicit solution of the parameter vector is thus obtained.

4. YIELD CURVE

The connection with the yield curve is established in this section. The maximum

smoothness forward rate curve is obtained using the following fourth degree polynomial.

when t

i i i i

it bt ct dt e

a t

f_{( )}= 4 + 3+ 2 + +

i

From (2), _{i i} , i=1, 2,…, m. t t t y ds s f i ) ( ) ( 0 =

∫

We may write this as

). ( 1 ) ( 1 ) ( 1 1 2 2 1 3 3 1 4 4 1 5 5 1 1 2 3 4 1

∑

∑ ∫

= = ∆ + ∆ + ∆ + ∆ + ∆ = + + + + = − i j j j j j j j j j j j i i j t t j j j j j i i e d c b a t ds e s d s c s b s a t t y j

j (24)

Thus (24) allows the computation of the yield curve based on the maximum smoothness

forward rate curve where the coefficients x are determined.

From (2), we can also show that for each segment i=1,2,…,m, the maximum forward

rate curve smoothness is equivalent to

.

(

)

∫

∫

− − ′′′ + ′′ = ′′ i i i i i i t t x t t

x f sds y s sy s ds

1 1

2

2_{( ) min 3 ( ) ( )}

min

Thus in general, maximum smoothness of forward rate curve will be different from maximum

smoothness of yield curve. However, if y′′′is small, then the maximizing of forward rate curve

smoothness will also result in a smooth yield curve. Obviously the continuity conditions for the

forward rate curve at each segment will be different from those of the yield curve segments,

although this may not have a major impact if the adjacent coefficients x_i and x_i-1 are close in

values.

5. CONCLUSIONS

This paper introduces a simple method to estimate the maximum smoothness forward

rate curve and the corresponding yield curve. The integral of the squared second-order

derivative of the curve function is defined as a measure of smoothness. We provide a correct

fourth-degree polynomial. We also provide the numerical algorithm to compute the

corresponding yield and the forward rate curves.

Increasingly many of the interest rate derivatives pricing and hedging depend on the

availability of many points on the forward rate curve or the yield curve than is provided by the

observed finite number of points in the market. The ability to fit an intuitively appropriate

ENDNOTES

1. Other approaches include Schwartz (1989)’s cubic splines and McCulloch (1975)’s polynomial spline functions to fit the observed data. However, the use of polynomial function to fit the entire yield curve may lead to unacceptable yield patterns. Vasicek and Fong (1982) use the exponential spline for the discount function, and choose the cubic form as the lowest odd order form from continuous derivatives. Delbaen and Lorimier (1992) introduce the discrete approach to estimate the yield curve by minimizing the difference between two adjacent forward rates. Frishling and Yamamula (1996) use a similar approach on the coupon bond prices.

REFERENCES

Adams, Kenneth J. and Donald R. Van Deventer, “Fitting yield curves and forward rate curves with maximum smoothness,” Journal of Fixed Income, V4 (1994), I1, 52-62.

Delbaen, F., and Sabine Lorimier, “Estimation of the Yield Curve and the Forward Rate Curve Starting from a Finite Number of Observations,” Insurance: Mathematics and Economics, V11 (1992), 259-269.

Frishling, Volf, and Yamamura, Junko, “Fitting a smooth forward rate curve to coupon instruments,” Journal of Fixed Income, V6 I2 (1996), 97-103.

Heath, D., R. Jarrow, and A. Morton, “Bond Pricing and the Term Structure of Interest Rates: A New Methodology for Contingent Claims Valuation,” Econometrica, V60 (1992), 77-105.

Hull, John, and Alan White, “One-Factor rate Models and the Valuation of Interest-rate Derivative Securities,” Journal of Financial and Quantitative Analysis, V28 (1993), 235-254.

McCulloch, J. H., “the Tax-adjusted Yield Curve,” Journal of Finance, V30 (1975), 811-829.

Schwartz, H. R., Numerical Methods: A Comprehensive Introduction. New York: John Wiley & Sons, 1989.

Vasicek, Oldrich A., “An Equilibrium Characterization of the Term Structure,” Journal of

Financial Economics, V5 (1977), 177-188.