Exam 25: Estimating the Yield Curve

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Exponential splines

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Let two time points, t1t _ { 1 } and t2t _ { 2 } , on a yield curve be given, and let y(t1)y \left( t _ { 1 } \right) , y(t2)y \left( t _ { 2 } \right) be the yields at these maturities. You want to draw an interpolating curve between these maturities and are considering three alternatives: -Linear (L): y(t)=y(t1)+x(tt1)y ( t ) = y \left( t _ { 1 } \right) + x \cdot \left( t - t _ { 1 } \right) . -Exponential (E): y(t)=y(t1)ex(tt1)y ( t ) = y \left( t _ { 1 } \right) e ^ { x \left( t - t _ { 1 } \right) } . -Logarithmic (G): y(t)=y(t1)[1+ln(1+x(tt1))]y ( t ) = y \left( t _ { 1 } \right) \left[ 1 + \ln \left( 1 + x \cdot \left( t - t _ { 1 } \right) \right) \right] . Since the interpolated curves will not coincide perfectly except at the two end-points, interpolated yields will be higher under some methods versus the others. What is the rank-ordering of size of interpolated yields?

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The Nelson-Siegel-Svensson model is

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Which of the following is NOT a benefit of the spline method of estimating discount functions across a spectrum of maturities?

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A major advantage of the cubic spline approach is that it can be estimated using ordinary least squares (OLS) regression. Which of the following is NOT related to this estimation feature?

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Which of the following is a required property of the cubic spline approach to estimating the yield curve and discount functions d(t)d ( t ) ? Recall that the approach fits functions dk(t)d _ { k } ( t ) to kk regions between (k+1)( k + 1 ) knot points.

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The Nelson-Siegel-Svensson model is

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Which of the following is not a feature of the exponential splines fitting approach?

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Which of the following is NOT a valid restriction or deficiency of the bootstrap method of estimating the discount function?

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You are given two discount bonds of one-year and two-year maturities, with prices of 95 and 90, respectively. A third bond of three-year maturity and an annual coupon of 8% is trading at par. What is the three-year continuously-compounded zero-coupon rate?

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In the Nelson-Siegel framework, which of the following statements is valid? Let the notation used to describe the fitting function be the following: A(t)=a+bet/d+c(t/d)et/dA ( t ) = a + b e ^ { - t / d } + c ( t / d ) e ^ { - t / d }

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Under exponential interpolation, if he tit _ { i } -tear yield is y(ti)y \left( t _ { i } \right) , i=1,2i = 1,2 , then the interpolated yield for tt lying between t1t _ { 1 } and t2t _ { 2 } is given by y(t)=y(t1)ex(tt1)y ( t ) = y \left( t _ { 1 } \right) e ^ { x \cdot \left( t - t _ { 1 } \right) } where xx is a parameter. Suppose the yield at one year is 4% and the yield at two years is 5%. Then, the closest yield at one and a half years, using exponential interpolation in time tt , is

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Which of the following is NOT a property of a cubic spline?

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The Nelson-Siegel algorithm is primarily used for fitting a smooth curve to the following:

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In selecting the placement of knot points in implementing cubic splines, it is common to

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One of the deficiencies of the bootstrap method is that it only returns discount functions that are on discrete dates. One approach to address this problem for cashflows that do not fall on these dates is to split them into allocations to near dates for which discount functions are available. Suppose we have discount functions d(t)d ( t ) , where d(1)=0.95d ( 1 ) = 0.95 and d(2)=0.90d ( 2 ) = 0.90 . Assuming continuous compounding, how will a cashflow at t=1.5t = 1.5 years of $100 be allocated to t=1t = 1 and t=2t = 2 years such that the present value and duration of cashflow remains the same? Assume that the forward rate between 1 and 2 years is constant. The allocation of cashflows is:

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The Nelson-Siegel model is extended to what is known as the Nelson-Siegel-Svensson model. The primary additional property that is added by this extension is:

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Under logarithmic interpolation, if he tit _ { i } -tear yield is y(ti)y \left( t _ { i } \right) , i=1,2i = 1,2 , then the interpolated yield for tt lying between t1t _ { 1 } and t2t _ { 2 } is given by y(t)=y(t1)[1+ln(1+x(tt1))]y ( t ) = y \left( t _ { 1 } \right) \left[ 1 + \ln \left( 1 + x \cdot \left( t - t _ { 1 } \right) \right) \right] where xx is a parameter. Suppose the yield at one year is 4% and the yield at two years is 5%. Then, the closest yield at one and a half years, using logarithmic interpolation in time tt , is

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In the cubic splines technique, which of the following is a benefit of adding more knot points to the algorithm?

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