This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

Calibrating Caplets Using the Normal (Bachelier) Model

This example shows how to use hwcalbycap to calibrate market data with the Normal (Bachelier) model to price caplets. Use the Normal (Bachelier) model to perform calibrations when working with negative interest rates, strikes, and normal implied volatilities.

Consider a cap with these parameters:

Settle = 'Dec-30-2016';
Maturity = 'Dec-30-2019';
Strike = -0.001075;
Reset = 2;
Principal = 100;
Basis = 0;

The caplets and market data for this example are defined as:

capletDates = cfdates(Settle, Maturity, Reset, Basis);

% Market data information
MarketStrike = [-0.0013; 0];
MarketMat =  {'30-Jun-2017';'30-Dec-2017';'30-Jun-2018'; '30-Dec-2018';'30-Jun-2019'; '30-Dec-2019'};
MarketVol = [0.184 0.2329 0.2398 0.2467 0.2906 0.3348;   % First row in table corresponding to Strike 1
             0.217 0.2707 0.2760 0.2814 0.3160 0.3508];  % Second row in table corresponding to Strike 2
ans =

  6×11 char array


Define the RateSpec.

Rates= [-0.002210;-0.002020;-0.00182;-0.001343;-0.001075];
ValuationDate = 'Dec-30-2016';
EndDates = {'30-Jun-2017';'Dec-30-2017';'30-Jun-2018';'Dec-30-2018';'Dec-30-2019'};
Compounding = 2;
Basis = 0;

RateSpec = intenvset('ValuationDate', ValuationDate, ...
'StartDates', ValuationDate, 'EndDates', EndDates, ...
'Rates', Rates, 'Compounding', Compounding, 'Basis', Basis);

Use hwcalbycap to find values for the volatility parameters Alpha and Sigma using the Normal (Bachelier) model.

format short
warning ('off','fininst:hwcalbycapfloor:NoConverge')
[Alpha, Sigma, OptimOut] = hwcalbycap(RateSpec, MarketStrike, MarketMat,...
MarketVol, Strike, Settle, Maturity, 'Reset', Reset, 'Principal', Principal,...
'Basis', Basis, 'OptimOptions', o, 'model', 'normal')
Local minimum possible.

lsqnonlin stopped because the size of the current step is less than
the default value of the step size tolerance.

Alpha =


Sigma =


OptimOut = 

  struct with fields:

     resnorm: 0.0013
    residual: [5×1 double]
    exitflag: 2
      output: [1×1 struct]
      lambda: [1×1 struct]
    jacobian: [5×2 double]

The OptimOut.residual field of the OptimOut structure is the optimization residual. This value contains the difference between the Normal (Bachelier) caplets and those calculated during the optimization. Use the OptimOut.residual value to calculate the percentual difference (error) compared to Normal (Bachelier) caplet prices, and then decide whether the residual is acceptable. There is almost always some residual, so decide if it is acceptable to parameterize the market with a single value of Alpha and Sigma.

Price the caplets using the market data and Normal (Bachelier) model to obtain the reference caplet values. To determine the effectiveness of the optimization, calculate reference caplet values using the Normal (Bachelier) formula and the market data. Note, you must first interpolate the market data to obtain the caplets for calculation.

MarketMatNum = datenum(MarketMat);
[Mats, Strikes] = meshgrid(MarketMatNum, MarketStrike);
FlatVol = interp2(Mats, Strikes, MarketVol, datenum(Maturity), Strike, 'spline');

[CapPrice, Caplets] = capbynormal(RateSpec, Strike, Settle, Maturity, FlatVol,...
'Reset', Reset, 'Basis', Basis, 'Principal', Principal);
Caplets = Caplets(2:end)'
Caplets =


Compare the optimized values and Normal (Bachelier) values, and display the results graphically. After calculating the reference values for the caplets, compare the values analytically and graphically to determine whether the calculated single values of Alpha and Sigma provide an adequate approximation.

OptimCaplets = Caplets+OptimOut.residual;

disp('   ');
disp(' Bachelier   Calibrated Caplets');
disp([Caplets        OptimCaplets])

plot(MarketMatNum(2:end), Caplets, 'or', MarketMatNum(2:end), OptimCaplets, '*b');
datetick('x', 2)
xlabel('Caplet Maturity');
ylabel('Caplet Price');
ylim ([0 16]);
title('Bachelier and Calibrated Caplets');
h = legend('Bachelier Caplets', 'Calibrated Caplets');
set(h, 'color', [0.9 0.9 0.9]);
set(h, 'Location', 'SouthEast');
set(gcf, 'NumberTitle', 'off')
grid on
 Bachelier   Calibrated Caplets
    6.7100    6.7293
    9.5919    9.6086
   11.6705   11.6820
   13.5914   13.5848
   15.0481   15.0256

Was this topic helpful?