Fixed Income > YCM 2001 - Interpolation Techniques
-
Upload
api-27174321 -
Category
Documents
-
view
924 -
download
4
Transcript of Fixed Income > YCM 2001 - Interpolation Techniques
Interpolation Techniques
Copyright © 1996-2006Investment Analytics
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 2
Interpolation Techniques
Why interpolate?Straight line interpolationCubic spline interpolationBasis spline interpolation
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 3
Why Interpolate
StructuringProject security cash flowsNeed forward rates on coupon dates
ValuationNeed spot rates on coupon dates
In either case coupon dates may not coincide with dates for which zero-coupon yields are known.
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 4
Interpolation MethodsStraight LinePolynomial
Single high order polynomialUnstable between points and at ends
Splined polynomialLow order polynomials linked together
Basis SplinesRepresent discount function as weighted sum of other functions
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 5
Straight Line Interpolation –Pros and ConsSimple to estimate intermediate points on curveNot accurate for undulating curvesGives different results on discount factorsProduces discontinuous forward rate curve
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 6
Linear Interpolation
Intermediate values lie on a straight line between the nearest data points.
Ri = R1 + (R2 - R1 ) x (Ti - T1) / (T2 - T1)
T1
Ti
T2
R1
Ri
R2
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 7
Linear Interpolation: Rates or Discount Factors?
If interest rates lie on a straight line, discount factors do notExample:
Using Rates Using DF’sR1 = R2 = 5.00% D1 = 0.9877T1 = 90 D2 = 0.9756T2 = 180 Di = 0.9836Ti = 120Ri = 5.00% Ri = 4.99%
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 8
Linear and Exponential InterpolationLinear interpolation on continuously compounded interest rates is equivalent to exponential interpolation on discount factors
D e D eR R R
T TT T
D D D
R T R T
i
i
i
TT
TT
i i
1
1 2
1
2 1
1
1
2
1 1 2 2
1 2
21
= == − +
=−−
⇒ =
− −
−
,( )
( )
α α
α
α α
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 9
Cubic Spline Interpolation
A different cubic polynomial is fitted between each pair of data pointsThe polynomials are twice differentiableEnsures that:
The slope of the curve is smoothThe rate of change of the slope is smoothThe curves “join” at the end points
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 10
Cubic Spline Curve Fitting
Ri(t) = ai(t-ti)3 + bi (t-ti)2 + ci (t-ti) + di
Ri+1(t)
Ri-1(t)
5.00%
5.50%
6.00%
6.50%
7.00%
7.50%
8.00%
8.50%
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 11
Natural Splines
End Curve ConditionsConditions of the two ends of the yield curve must be specified for a solution.
Natural SplineSecond derivative (rate of change of the slope of the yield curve) equal to zero at both ends.
Slope of curve is constant at the endsYou typically only care about points in the belly of the curve
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 12
Cubic Splines – Pros & Cons
Smooth curve -twice differentiable at every data pointCan be used on both rates and DF’s Works for undulating curvesProduces continuous forward rate curveNot so easy to calculateCan suffer from oscillation
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 13
Lab: Building Yield Curves with Cubic Splines
Excel workbook; Yield Curve Modeling.xlsWorksheet: Cubic Spline CurveBuild 3m forward rate curve using:
Linearly interpolated DFsLinearly interpolated spot ratesCubic Spline interpolated spot rates
See Notes & Solution
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 14
Solution: Cubic SplineForward Curves
5.00%
5.50%
6.00%
6.50%
7.00%
7.50%
8.00%
8.50%
0 500 1000 1500 2000Days
Linear Interp on DF
Linear Interp on R
Cspline Interp on DF
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 15
Basis SplinesAnother widely used interpolation methodUsed for modeling discount functionTypically combined with regression analysis
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 16
Regression: More Payment Dates than Bonds
This is the usual case, as bond coupon dates fall on different days in the year.Have to represent discount factors by a function
Insufficient bonds to estimate model parametersSingular matrix
Use regression to determine parameters of the discount function
Then calculate discount factors on any chosen date
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 17
Representing the Discount Function by Basis Splines
Represent DF’s by function d(t):
Bond prices can be expressed as the sum of discounted cash flows:
Determine values of weights to fit bond prices to market data.
)()(1
tftd l
L
ll∑
=
= αl = 1...L: the number of basis spline functions f.α: weights applied to each function
)(11
tfCP l
L
ll
n
jiji ∑∑
==
= αC: Bond cash flowsP: Bond price
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 18
Estimating the Discount Function
Rearrange bond price equation:Sum of discounted cash flows
Sum of weighted cashflow x spline function
Spline functions are defined over the whole periodAs long as we have more bonds than weighting factors α,regression can be used.
)(11
tfCP l
L
ll
n
jiji ∑∑
==
= α
)(11
tfCP l
n
jij
L
lli ∑∑
==
= α
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 19
Basis Splines & Knot PointsBasis Splines
The discount function is a weighted average of a number of overlapping B-Splines.Cubic B-Spline functions usually selected.Individual spline functions are not linked.
Knot PointsEach spline function is non-zero over a well-defined interval.The start and end points of the splines are called “knot points”.
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 20
Basis Spline CurvesThe discount function is the weighted sum of individual splines
Knot Points
0.00E+00
2.00E-05
4.00E-05
6.00E-05
8.00E-05
1.00E-04
1.20E-04
1.40E-04
1.60E-04
1.80E-04
2.00E-04
0 500 1000 1500 2000 2500 3000
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 21
Selection of Knot Points
Results can be sensitive to placing of knot points
Unless there is an even distribution of bonds.
Important to have an equal number of bonds with maturities between each knot point.
Reduces estimation error.
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 22
Building a Zero Coupon Curve from Treasury Bonds
Often have more payment dates than bondsNo unique set of discount factors that will price all bonds
Use Regression AnalysisDetermine Least Squares Estimates of Discount Factors
Minimize the square of the difference between the observed bond prices and those based on estimated discount factors.
Discount factors must be linked by a functional formCubic splines have problems due to correlation.Basis splines are independent but watch “knot points”.
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 23
Lab: Building a Yield Curve with Basis Splines
Worksheet: Basis SplinesBuild yield curve using bond dataMethod:
Basis Splines & Regression
See Notes & Solution
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 24
Spot and Forward Rate Curves
5.0%
5.5%
6.0%
6.5%
7.0%
7.5%
0 500 1000 1500 2000 2500 3000
Spot
Forw ard
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 25
Confidence Intervals
3%
4%
4%
5%
5%
6%
6%
7%
7%
8%
0 500 1000 1500 2000 2500 3000
Spot Rate
Upper 95%
Low er 95%
Copyright © 1996-2006 Investment Analytics Interpolation Techniques Slide: 26
Interpolation Methods: Summary
Straight Line InterpolationInaccurate.Leads to discontinuous forward rates.
Cubic SplinesBetter than linear interpolation.Due to smoothness condition points on the yield curve are linkedtogether.Linking causes multicollinearity.Accuracy of and one discount factor cannot be determined.
Basis SplinesFunctions go to zero at defined points.Need to use a weighted combination of several B-Splines.