This example uses Financial Toolbox™ bond pricing functions to evaluate the impact of time-to-maturity and yield variation on the price of a bond portfolio. Also, this example shows how to visualize the price behavior of a portfolio of bonds over a wide range of yield curve scenarios, and as time progresses toward maturity.
Specify values for the settlement date, maturity date, face value, coupon rate, and coupon payment periodicity of a four-bond portfolio. For simplicity, accept default values for the end-of-month payment rule (rule in effect) and day-count basis (actual/actual). Also, synchronize the coupon payment structure to the maturity date (no odd first or last coupon dates). Any inputs for which defaults are accepted are set to empty matrices (
) as placeholders where appropriate. Also, specify the points on the yield curve for each bond.
Settle = '15-Jan-1995'; Maturity = datenum(['03-Apr-2020'; '14-May-2025'; ... '09-Jun-2019'; '25-Feb-2019']); Face = [1000; 1000; 1000; 1000]; CouponRate = [0; 0.05; 0; 0.055]; Periods = [0; 2; 0; 2]; Yields = [0.078; 0.09; 0.075; 0.085];
Use Financial Toolbox functions to calculate the true bond prices as the sum of the quoted price plus accrued interest.
[CleanPrice, AccruedInterest] = bndprice(Yields,... CouponRate,Settle, Maturity, Periods,... , , , , , , Face); Prices = CleanPrice + AccruedInterest
Prices = 145.2452 594.7757 165.8949 715.7584
Assume that the value of each bond is $25,000, and determine the quantity of each bond such that the portfolio value is $100,000.
BondAmounts = 25000 ./ Prices;
Compute the portfolio price for a rolling series of settlement dates over a range of yields. The evaluation dates occur annually on January 15, beginning on 15-Jan-1995 (settlement) and extending out to 15-Jan-2018. Thus, this step evaluates portfolio price on a grid of time of progression (
dT) and interest rates (
dy = -0.05:0.005:0.05; % Yield changes D = datevec(Settle); % Get date components dt = datenum(D(1):2018, D(2), D(3)); % Get evaluation dates [dT, dY] = meshgrid(dt, dy); % Create grid NumTimes = length(dt); % Number of time steps NumYields = length(dy); % Number of yield changes NumBonds = length(Maturity); % Number of bonds % Preallocate vector Prices = zeros(NumTimes*NumYields, NumBonds);
Now that the grid and price vectors have been created, compute the price of each bond in the portfolio on the grid one bond at a time.
for i = 1:NumBonds [CleanPrice, AccruedInterest] = bndprice(Yields(i)+... dY(:), CouponRate(i), dT(:), Maturity(i), Periods(i),... , , , , , , Face(i)); Prices(:,i) = CleanPrice + AccruedInterest; end
Scale the bond prices by the quantity of bonds and reshape the bond values to conform to the underlying evaluation grid.
Prices = Prices * BondAmounts; Prices = reshape(Prices, NumYields, NumTimes);
Plot the price of the portfolio as a function of settlement date and a range of yields, and as a function of the change in yield (
dY). This plot illustrates the interest-rate sensitivity of the portfolio as time progresses (
dT), under a range of interest-rate scenarios. With the following graphics commands, you can visualize the three-dimensional surface relative to the current portfolio value (that is, $100,000).
figure % Open a new figure window surf(dt, dy, Prices) % Draw the surface hold on % Add the current value for reference basemesh = mesh(dt, dy, 100000*ones(NumYields, NumTimes)); set(basemesh, 'facecolor', 'none'); set(basemesh, 'edgecolor', 'm'); set(gca, 'box', 'on'); dateaxis('x', 11); xlabel('Evaluation Date (YY Format)'); ylabel('Change in Yield'); zlabel('Portfolio Price'); hold off view(-25,25);
MATLAB® three-dimensional graphics allow you to visualize the interest-rate risk experienced by a bond portfolio over time. This example assumed parallel shifts in the term structure, but it might similarly have allowed other components to vary, such as the level and slope.