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This example shows how to construct a Diebold Li model of the US yield curve for each month from 1990 to 2010. This example also demonstrates how to forecast future yield curves by fitting an autoregressive model to the time series of each parameter.

The paper can be found here:

http://www.ssc.upenn.edu/~fdiebold/papers/paper49/Diebold-Li.pdf

The data used are monthly Treasury yields from 1990 through 2010 for tenors of 1 Mo, 3 Mo, 6 Mo, 1 Yr, 2 Yr, 3 Yr, 5 Yr, 7 Yr, 10 Yr, 20 Yr, 30 Yr.

Daily data can be found here:

Data is stored in a MATLAB® data file as a MATLAB `dataset`

object.

load Data_USYieldCurve % Extract data for the last day of each month MonthYearMat = repmat((1990:2010)',1,12)'; EOMDates = lbusdate(MonthYearMat(:),repmat((1:12)',21,1)); MonthlyIndex = find(ismember(Dataset.Properties.ObsNames,datestr(EOMDates))); Estimationdataset = Dataset(MonthlyIndex,:); EstimationData = double(Estimationdataset);

Diebold and Li start with the Nelson Siegel model

and rewrite it to be the following:

The above model allows the factors to be interpreted in the following way: Beta1 corresponds to the long term/level of the yield curve, Beta2 corresponds to the short term/slope, and Beta3 corresponds to the medium term/curvature. determines the maturity at which the loading on the curvature is maximized, and governs the exponential decay rate of the model.

Diebold and Li advocate setting to maximize the loading on the medium term factor, Beta3, at 30 months. This also transforms the problem from a nonlinear fitting to a simple linear regression.

% Explicitly set the time factor lambda lambda_t = .0609; % Construct a matrix of the factor loadings % Tenors associated with data TimeToMat = [3 6 9 12 24 36 60 84 120 240 360]'; X = [ones(size(TimeToMat)) (1 - exp(-lambda_t*TimeToMat))./(lambda_t*TimeToMat) ... ((1 - exp(-lambda_t*TimeToMat))./(lambda_t*TimeToMat) - exp(-lambda_t*TimeToMat))]; % Plot the factor loadings plot(TimeToMat,X) title('Factor Loadings for Diebold Li Model with time factor of .0609') xlabel('Maturity (months)') ylim([0 1.1]) legend({'Beta1','Beta2','Beta3'},'location','east')

A `DieboldLi`

object is developed to facilitate fitting the model from yield data. The `DieboldLi`

object inherits from the `IRCurve`

object, so the `getZeroRates`

, `getDiscountFactors`

, `getParYields`

, `getForwardRates`

, and `toRateSpec`

methods are all implemented. Additionally, the method `fitYieldsFromBetas`

is implemented to estimate the Beta parameters given a lambda parameter for observed market yields.

The `DieboldLi`

object is used to fit a Diebold Li model for each month from 1990 through 2010.

% Preallocate the Betas Beta = zeros(size(EstimationData,1),3); % Loop through and fit each end of month yield curve for jdx = 1:size(EstimationData,1) tmpCurveModel = DieboldLi.fitBetasFromYields(EOMDates(jdx),lambda_t*12,daysadd(EOMDates(jdx),30*TimeToMat),EstimationData(jdx,:)'); Beta(jdx,:) = [tmpCurveModel.Beta1 tmpCurveModel.Beta2 tmpCurveModel.Beta3]; end

The Diebold Li fits on selected dates are included here

PlotSettles = datenum({'30-May-1997','31-Aug-1998','29-Jun-2001','31-Oct-2005'}); figure for jdx = 1:length(PlotSettles) subplot(2,2,jdx) tmpIdx = find(strcmpi(Estimationdataset.Properties.ObsNames,datestr(PlotSettles(jdx)))); tmpCurveModel = DieboldLi.fitBetasFromYields(PlotSettles(jdx),lambda_t*12,... daysadd(PlotSettles(jdx),30*TimeToMat),EstimationData(tmpIdx,:)'); scatter(daysadd(PlotSettles(jdx),30*TimeToMat),EstimationData(tmpIdx,:)) hold on PlottingDates = (PlotSettles(jdx)+30:30:PlotSettles(jdx)+30*360)'; plot(PlottingDates,tmpCurveModel.getParYields(PlottingDates),'r-') title(['Yield Curve on ' datestr(PlotSettles(jdx))]) datetick end

The Diebold Li model can be used to forecast future yield curves. Diebold and Li propose fitting an AR(1) model to the time series of each Beta parameter. This fitted model can then be used to forecast future values of each parameter, and by extension, future yield curves.

For this example the MATLAB function `regress`

is used to estimate the parameters for an AR(1) model for each Beta.

The confidence intervals for the regression fit are also used to generate two additional yield curve forecasts that serve as additional possible scenarios for the yield curve.

The `MonthsLag`

variable can be adjusted to make different period ahead forecasts. For example, changing the value from `1`

to `6`

would change the forecast from a 1 month ahead to 6 month ahead forecast.

MonthsLag = 1; [tmpBeta,bint] = regress(Beta(MonthsLag+1:end,1),[ones(size(Beta(MonthsLag+1:end,1))) Beta(1:end-MonthsLag,1)]); ForecastBeta(1,1) = [1 Beta(end,1)]*tmpBeta; ForecastBeta_Down(1,1) = [1 Beta(end,1)]*bint(:,1); ForecastBeta_Up(1,1) = [1 Beta(end,1)]*bint(:,2); [tmpBeta,bint] = regress(Beta(MonthsLag+1:end,2),[ones(size(Beta(MonthsLag+1:end,2))) Beta(1:end-MonthsLag,2)]); ForecastBeta(1,2) = [1 Beta(end,2)]*tmpBeta; ForecastBeta_Down(1,2) = [1 Beta(end,2)]*bint(:,1); ForecastBeta_Up(1,2) = [1 Beta(end,2)]*bint(:,2); [tmpBeta,bint] = regress(Beta(MonthsLag+1:end,3),[ones(size(Beta(MonthsLag+1:end,3))) Beta(1:end-MonthsLag,3)]); ForecastBeta(1,3) = [1 Beta(end,3)]*tmpBeta; ForecastBeta_Down(1,3) = [1 Beta(end,3)]*bint(:,1); ForecastBeta_Up(1,3) = [1 Beta(end,3)]*bint(:,2); % Forecasted yield curve figure Settle = daysadd(EOMDates(end),30*MonthsLag); DieboldLi_Forecast = DieboldLi('ParYield',Settle,[ForecastBeta lambda_t*12]); DieboldLi_Forecast_Up = DieboldLi('ParYield',Settle,[ForecastBeta_Up lambda_t*12]); DieboldLi_Forecast_Down = DieboldLi('ParYield',Settle,[ForecastBeta_Down lambda_t*12]); PlottingDates = (Settle+30:30:Settle+30*360)'; plot(PlottingDates,DieboldLi_Forecast.getParYields(PlottingDates),'b-') hold on plot(PlottingDates,DieboldLi_Forecast_Up.getParYields(PlottingDates),'r-') plot(PlottingDates,DieboldLi_Forecast_Down.getParYields(PlottingDates),'r-') title(['Diebold Li Forecasted Yield Curves on ' datestr(EOMDates(end)) ' for ' datestr(Settle)]) legend({'Forecasted Curve','Additional Scenarios'},'location','southeast') datetick

This example is based on the following paper:

[1] Francis X. Diebold, Canlin Li, Forecasting the term structure of government bond yields, Journal of Econometrics, Volume 130, Issue 2, February 2006, Pages 337-364