/** Check extrapolation is recovered for shifted lognormal model extrapolation */
@Test
public void functionRecoverySLNTest() {
final double forward = 1.0;
final double expiry = 3.0;
int nSamples = 11;
double[] strikes = new double[nSamples];
double[] vols = new double[nSamples];
final double muLeft = 0.4;
final double thetaLeft = 0.5;
// Expected left extrapolation
Function1D<Double, Double> left =
new Function1D<Double, Double>() {
@Override
public Double evaluate(Double strike) {
return ShiftedLogNormalTailExtrapolation.impliedVolatility(
forward, strike, expiry, muLeft, thetaLeft);
}
};
final double muRight = -0.3;
final double thetaRight = 0.5;
// Expected right extrapolation
Function1D<Double, Double> right =
new Function1D<Double, Double>() {
@Override
public Double evaluate(Double strike) {
return ShiftedLogNormalTailExtrapolation.impliedVolatility(
forward, strike, expiry, muRight, thetaRight);
}
};
for (int i = 0; i < 5; ++i) {
double strike = forward * (0.75 + 0.05 * i);
vols[i] = left.evaluate(strike);
strikes[i] = strike;
}
for (int i = 6; i < nSamples; ++i) {
double strike = forward * (0.75 + 0.05 * i);
vols[i] = right.evaluate(strike);
strikes[i] = strike;
}
strikes[5] = forward;
vols[5] = 0.5 * (vols[4] + vols[6]);
ShiftedLogNormalExtrapolationFunctionProvider extapSLN =
new ShiftedLogNormalExtrapolationFunctionProvider();
SmileInterpolatorSABRWithExtrapolation interpSLN =
new SmileInterpolatorSABRWithExtrapolation(extapSLN);
InterpolatedSmileFunction funcSLN =
new InterpolatedSmileFunction(interpSLN, forward, strikes, expiry, vols);
double[] keys = new double[] {forward * 0.1, forward * 0.5, forward * 0.66};
for (int i = 0; i < keys.length; ++i) {
assertEquals(left.evaluate(keys[i]), funcSLN.getVolatility(keys[i]), 1.e-2);
}
keys = new double[] {forward * 1.31, forward * 1.5, forward * 2.61, forward * 15.0};
for (int i = 0; i < keys.length; ++i) {
assertEquals(right.evaluate(keys[i]), funcSLN.getVolatility(keys[i]), 1.e-2);
}
}
/**
* @param x The array of data, not null. Must contain at least two data points
* @return The Pearson first skewness coefficient
*/
@Override
public Double evaluate(final double[] x) {
Validate.notNull(x);
Validate.isTrue(
x.length > 1,
"Need at least two data points to calculate Pearson first skewness coefficient");
return 3 * (MEAN.evaluate(x) - MODE.evaluate(x)) / STD_DEV.evaluate(x);
}
static {
final TreeMap<Double, Double> data = new TreeMap<>();
final TreeMap<Double, Double> transformedData = new TreeMap<>();
double x;
for (int i = 0; i < 10; i++) {
x = Double.valueOf(i);
data.put(x, FUNCTION.evaluate(x));
transformedData.put(x, Math.log(FUNCTION.evaluate(x)));
}
MODEL = LINEAR.getDataBundle(data);
TRANSFORMED_MODEL = INTERPOLATOR.getDataBundle(transformedData);
}
/**
* General method when you wish to compute the expected variance from a log-moneyness
* parametrised surface to within a certain tolerance
*
* @param surface log-moneyness parametrised volatility surface
* @return expected variance
*/
@SuppressWarnings("synthetic-access")
@Override
public Double visitLogMoneyness(final BlackVolatilitySurfaceLogMoneyness surface) {
final double atmVol = surface.getVolatilityForLogMoneyness(_t, 0.0);
if (_t < 1e-4) {
return atmVol * atmVol;
}
final double rootT = Math.sqrt(_t);
final double invNorTol = NORMAL.getInverseCDF(_tol);
final Function1D<Double, Double> integrand = getLogMoneynessIntegrand(surface);
double putPart;
if (_addResidual) {
putPart = _integrator.integrate(integrand, Math.log(_lowStrikeCutoff / _f), 0.0);
putPart += _residual;
} else {
final double l = invNorTol * atmVol * rootT; // initial estimate of lower limit
putPart = _integrator.integrate(integrand, l, 0.0);
double rem = integrand.evaluate(l);
double error = rem / putPart;
int step = 1;
while (error > _tol) {
putPart += _integrator.integrate(integrand, (step + 1) * l, step * l);
step++;
rem = integrand.evaluate((step + 1) * l);
error = rem / putPart;
}
putPart +=
rem; // add on the (very small) remainder estimate otherwise we'll always underestimate
// variance
}
final double u =
_f * Math.exp(-invNorTol * atmVol * rootT); // initial estimate of upper limit
double callPart = _integrator.integrate(integrand, 0.0, u);
double rem = integrand.evaluate(u);
double error = rem / callPart;
int step = 1;
while (error > _tol) {
callPart += _integrator.integrate(integrand, step * u, (1 + step) * u);
step++;
rem = integrand.evaluate((1 + step) * u);
error = rem / putPart;
}
// callPart += rem;
// don't add on the remainder estimate as it is very conservative, and likely too large
return 2 * (putPart + callPart) / _t;
}
private boolean getNextPosition(
final Function1D<DoubleMatrix1D, Double> function,
final Function1D<DoubleMatrix1D, DoubleMatrix1D> grad,
final DataBundle data) {
final DoubleMatrix1D p = getDirection(data);
if (data.getLambda0() < 1.0) {
data.setLambda0(1.0);
} else {
data.setLambda0(data.getLambda0() * BETA);
}
updatePosition(p, function, data);
final double g1 = data.getG1();
// the function is invalid at the new position, try to recover
if (Double.isInfinite(g1) || Double.isNaN(g1)) {
bisectBacktrack(p, function, data);
}
if (data.getG1() > data.getG0() / (1 + ALPHA * data.getLambda0())) {
quadraticBacktrack(p, function, data);
int count = 0;
while (data.getG1() > data.getG0() / (1 + ALPHA * data.getLambda0())) {
if (count > 5) {
return false;
}
cubicBacktrack(p, function, data);
count++;
}
}
final DoubleMatrix1D deltaX = data.getDeltaX();
data.setX((DoubleMatrix1D) MA.add(data.getX(), deltaX));
data.setG0(data.getG1());
final DoubleMatrix1D gradNew = grad.evaluate(data.getX());
data.setDeltaGrad((DoubleMatrix1D) MA.subtract(gradNew, data.getGrad()));
data.setGrad(gradNew);
return true;
}
@Test
/** Tests the present value. */
public void presentValueNoNotional() {
final MultipleCurrencyAmount pv = METHOD.presentValue(ZERO_COUPON_CAP, BLACK_INFLATION);
final double timeToMaturity =
ZERO_COUPON_CAP.getReferenceEndTime() - ZERO_COUPON_CAP.getLastKnownFixingTime();
final double df =
MARKET
.getCurve(ZERO_COUPON_CAP.getCurrency())
.getDiscountFactor(ZERO_COUPON_CAP.getPaymentTime());
final double finalIndex =
MARKET.getCurve(PRICE_INDEX_EUR).getPriceIndex(ZERO_COUPON_CAP.getReferenceEndTime());
final double forward = finalIndex / INDEX_1MAY_2008;
final EuropeanVanillaOption option =
new EuropeanVanillaOption(
Math.pow(1 + ZERO_COUPON_CAP.getStrike(), ZERO_COUPON_CAP.getMaturity()),
timeToMaturity,
ZERO_COUPON_CAP.isCap());
final double volatility =
BLACK_INFLATION
.getBlackParameters()
.getVolatility(ZERO_COUPON_CAP.getReferenceEndTime(), ZERO_COUPON_CAP.getStrike());
final BlackFunctionData dataBlack = new BlackFunctionData(forward, 1.0, volatility);
final Function1D<BlackFunctionData, Double> func = BLACK_FUNCTION.getPriceFunction(option);
final double pvExpected =
df
* func.evaluate(dataBlack)
* ZERO_COUPON_CAP.getNotional()
* ZERO_COUPON_CAP.getPaymentYearFraction();
assertEquals(
"Zero-coupon inflation DiscountingMethod: Present value",
pvExpected,
pv.getAmount(ZERO_COUPON_CAP.getCurrency()),
TOLERANCE_PV);
}
protected double getResidual(
final double fwd, final double expiry, final double[] ks, final double[] vols) {
// Check for trivial case where cutoff is so low that there's no effective value in the option
final double cutoffPrice =
BlackFormulaRepository.price(fwd, ks[0], expiry, vols[0], ks[0] > fwd);
if (CompareUtils.closeEquals(cutoffPrice, 0)) {
return 0.0; // i.e. the tail function is never used
}
// The typical case - fit a ShiftedLognormal to the two strike-vol pairs
final ShiftedLognormalVolModel leftExtrapolator =
new ShiftedLognormalVolModel(fwd, expiry, ks[0], vols[0], ks[1], vols[1]);
// Now, handle behaviour near zero strike. ShiftedLognormalVolModel has non-zero put price for
// zero strike.
// What we do is to find the strike, k_min, at which f(k) = p(k)/k^2 begins to blow up, by
// finding the minimum of this function, k_min
// then setting f(k) = f(k_min) for k < k_min. This ensures the implied volatility and the
// integrand are well behaved in the limit k -> 0.
final Function1D<Double, Double> shiftedLnIntegrand =
new Function1D<Double, Double>() {
@Override
public Double evaluate(final Double strike) {
return leftExtrapolator.priceFromFixedStrike(strike) / (strike * strike);
}
};
final double kMin = new BrentMinimizer1D().minimize(shiftedLnIntegrand, EPS, EPS, ks[0]);
final double fMin = shiftedLnIntegrand.evaluate(kMin);
double res = fMin * kMin; // the (hopefully) very small rectangular bit between zero and kMin
res += _integrator.integrate(shiftedLnIntegrand, kMin, ks[0]);
return res;
}
@Test
public void test() {
final int n = 12;
final GaussianQuadratureData f1 = GAUSS_LEGENDRE.generate(n);
final GaussianQuadratureData f2 = GAUSS_JACOBI_GL_EQUIV.generate(n);
final GaussianQuadratureData f3 = GAUSS_JACOBI_CHEBYSHEV_EQUIV.generate(n);
final double[] w1 = f1.getWeights();
final double[] w2 = f2.getWeights();
final double[] x1 = f1.getAbscissas();
final double[] x2 = f2.getAbscissas();
assertTrue(w1.length == w2.length);
assertTrue(x1.length == x2.length);
for (int i = 0; i < n; i++) {
assertEquals(w1[i], w2[i], EPS);
assertEquals(x1[i], -x2[i], EPS);
}
final double[] w3 = f3.getWeights();
final double[] x3 = f3.getAbscissas();
final double chebyshevWeight = Math.PI / n;
final Function1D<Integer, Double> chebyshevAbscissa =
new Function1D<Integer, Double>() {
@Override
public Double evaluate(final Integer x) {
return -Math.cos(Math.PI * (x + 0.5) / n);
}
};
for (int i = 0; i < n; i++) {
assertEquals(chebyshevWeight, w3[i], EPS);
assertEquals(chebyshevAbscissa.evaluate(i), -x3[i], EPS);
}
}
/**
* Test interpolation part, essentially consistent with the super class (where extrapolation is
* absent) iff local fitting is applied. If global fit is used, resulting interpolation is tested
* with larger tolerance
*/
@Test
public void interpolationTest() {
double eps = 1.0e-14;
double expiry = 1.2;
double forward = 1.7;
int nStrikes = 11;
double[] strikes = new double[nStrikes];
double[] impliedVols =
new double[] {
2.17, 1.92, 1.702, 1.545, 1.281, 0.912, 0.9934, 1.0878, 1.1499, 1.2032, 1.242
};
for (int i = 0; i < nStrikes; ++i) {
strikes[i] = forward * (0.85 + i * 0.05);
}
WeightingFunction weight = LinearWeightingFunction.getInstance();
int seed = 4729;
double beta = 0.95;
ShiftedLogNormalExtrapolationFunctionProvider extapQuiet =
new ShiftedLogNormalExtrapolationFunctionProvider("Quiet");
SmileInterpolatorSABRWithExtrapolation interpQuiet =
new SmileInterpolatorSABRWithExtrapolation(
seed, new SABRHaganVolatilityFunction(), beta, weight, extapQuiet);
InterpolatedSmileFunction funcQuiet =
new InterpolatedSmileFunction(interpQuiet, forward, strikes, expiry, impliedVols);
SmileInterpolatorSABR sabr =
new SmileInterpolatorSABR(seed, new SABRHaganVolatilityFunction(), beta, weight);
Function1D<Double, Double> volFunc =
sabr.getVolatilityFunction(forward, strikes, expiry, impliedVols);
int nKeys = 20;
for (int i = 0; i < nKeys + 1; ++i) {
Double key = strikes[0] + (strikes[nStrikes - 1] - strikes[0]) * i / nKeys;
assertEquals(volFunc.evaluate(key), funcQuiet.getVolatility(key), eps);
}
SmileInterpolatorSABRWithExtrapolation interpGlobal1 =
new SmileInterpolatorSABRWithExtrapolation(new SABRPaulotVolatilityFunction(), extapQuiet);
SmileInterpolatorSABRWithExtrapolation interpGlobal2 =
new SmileInterpolatorSABRWithExtrapolation(
new SABRBerestyckiVolatilityFunction(), extapQuiet);
SmileInterpolatorSABRWithExtrapolation interpGlobal3 =
new SmileInterpolatorSABRWithExtrapolation(
new SABRHaganAlternativeVolatilityFunction(), extapQuiet);
InterpolatedSmileFunction funcGlobal1 =
new InterpolatedSmileFunction(interpGlobal1, forward, strikes, expiry, impliedVols);
InterpolatedSmileFunction funcGlobal2 =
new InterpolatedSmileFunction(interpGlobal2, forward, strikes, expiry, impliedVols);
InterpolatedSmileFunction funcGlobal3 =
new InterpolatedSmileFunction(interpGlobal3, forward, strikes, expiry, impliedVols);
for (int i = 0; i < nKeys + 1; ++i) {
Double key = strikes[0] + (strikes[nStrikes - 1] - strikes[0]) * i / nKeys;
double ref = funcQuiet.getVolatility(key);
assertEquals(ref, funcGlobal1.getVolatility(key), 1.5 * ref * 1.0e-1);
assertEquals(ref, funcGlobal2.getVolatility(key), ref * 1.0e-1);
assertEquals(ref, funcGlobal3.getVolatility(key), ref * 1.0e-1);
}
}
@Test
/**
* Tests the 'In-Out Parity' condition: Without rebates, the price of a Knock-In plus a Knock-Out
* of arbitrary barrier level must equal that of the underlying vanilla option
*/
public void inOutParityWithoutRebate() {
// Vanilla
final Function1D<BlackFunctionData, Double> fcnVanillaCall =
BLACK_FUNCTION.getPriceFunction(VANILLA_CALL_K100);
final double pxVanillaCall = fcnVanillaCall.evaluate(DATA_BLACK);
// Barriers without rebate
final double noRebate = 0.0;
final double priceDownIn =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100,
BARRIER_DOWN_IN,
noRebate,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY);
final double priceDownOut =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100,
BARRIER_DOWN_OUT,
noRebate,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY);
assertEquals(
"Knock In-Out Parity fails", 1.0, pxVanillaCall / (priceDownIn + priceDownOut), 1.e-6);
}
@Test
/**
* Tests the 'In-Out Parity' condition: Knock-In's pay rebate at maturity if barrier isn't hit.
* Knock-Out pays at moment barrier is hit. The discounting issue can be sidestepped by setting
* rates to 0.
*/
public void inOutParityWithRebate() {
// Vanilla
final Function1D<BlackFunctionData, Double> fcnVanillaCall =
BLACK_FUNCTION.getPriceFunction(VANILLA_CALL_K100);
final BlackFunctionData zeroRatesMarket = new BlackFunctionData(SPOT, 1.0, VOLATILITY);
final double pxVanillaCall = fcnVanillaCall.evaluate(zeroRatesMarket);
// Barriers with rebate
final double priceDownInRebate =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, BARRIER_DOWN_IN, REBATE, SPOT, 0.0, 0.0, VOLATILITY);
final double priceDownOutRebate =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, BARRIER_DOWN_OUT, REBATE, SPOT, 0.0, 0.0, VOLATILITY);
assertEquals(
"Knock In-Out Parity fails",
1.0,
(pxVanillaCall + REBATE) / (priceDownInRebate + priceDownOutRebate),
1.e-6);
}
@Override
public DoubleMatrix1D minimize(
final Function1D<DoubleMatrix1D, Double> function,
final Function1D<DoubleMatrix1D, DoubleMatrix1D> grad,
final DoubleMatrix1D startPosition) {
final DataBundle data = new DataBundle();
final double y = function.evaluate(startPosition);
data.setX(startPosition);
data.setG0(y * y);
data.setGrad(grad.evaluate(startPosition));
data.setInverseHessianEsimate(getInitializedMatrix(startPosition));
if (!getNextPosition(function, grad, data)) {
throw new MathException(
"Cannot work with this starting position. Please choose another point");
}
int count = 0;
int resetCount = 1;
while (!isConverged(data)) {
if ((resetCount) % RESET_FREQ == 0) {
data.setInverseHessianEsimate(getInitializedMatrix(startPosition));
resetCount = 1;
} else {
_hessainUpdater.update(data);
}
if (!getNextPosition(function, grad, data)) {
data.setInverseHessianEsimate(getInitializedMatrix(startPosition));
resetCount = 1;
if (!getNextPosition(function, grad, data)) {
throw new MathException("Failed to converge in backtracking");
}
}
count++;
resetCount++;
if (count > _maxSteps) {
throw new MathException(
"Failed to converge after "
+ _maxSteps
+ " iterations. Final point reached: "
+ data.getX().toString());
}
}
return data.getX();
}
protected void updatePosition(
final DoubleMatrix1D p,
final Function1D<DoubleMatrix1D, Double> function,
final DataBundle data) {
final double lambda0 = data.getLambda0();
final DoubleMatrix1D deltaX = (DoubleMatrix1D) MA.scale(p, lambda0);
final DoubleMatrix1D xNew = (DoubleMatrix1D) MA.add(data.getX(), deltaX);
data.setDeltaX(deltaX);
data.setG2(data.getG1());
final double y = function.evaluate(xNew);
data.setG1(y * y);
}
@Test
public void presentValue() {
final MultipleCurrencyAmount pvMethod =
METHOD_BLACK.presentValue(SWAPTION_LONG_REC, BLACK_MULTICURVES);
final double forward = SWAPTION_LONG_REC.getUnderlyingSwap().accept(PRDC, MULTICURVES);
final double pvbp =
METHOD_SWAP.presentValueBasisPoint(SWAPTION_LONG_REC.getUnderlyingSwap(), MULTICURVES);
final double volatility =
BLACK.getVolatility(
SWAPTION_LONG_REC.getTimeToExpiry(), SWAPTION_LONG_REC.getMaturityTime());
final BlackPriceFunction blackFunction = new BlackPriceFunction();
final BlackFunctionData dataBlack = new BlackFunctionData(forward, pvbp, volatility);
final Function1D<BlackFunctionData, Double> func =
blackFunction.getPriceFunction(SWAPTION_LONG_REC);
final double pvExpected = func.evaluate(dataBlack);
assertEquals(
"Swaption Black method: present value", pvExpected, pvMethod.getAmount(EUR), TOLERANCE_PV);
}
/**
* Computes the present value of the Physical delivery swaption through approximation..
*
* @param swaption The swaption.
* @param cfe The swaption cash flow equiovalent.
* @param g2Data The G2++ parameters and the curves.
* @return The present value.
*/
public CurrencyAmount presentValue(
final SwaptionPhysicalFixedIbor swaption,
final AnnuityPaymentFixed cfe,
final G2ppPiecewiseConstantDataBundle g2Data) {
YieldAndDiscountCurve dsc =
g2Data.getCurve(swaption.getUnderlyingSwap().getFixedLeg().getDiscountCurve());
int nbCf = cfe.getNumberOfPayments();
double[] cfa = new double[nbCf];
double[] t = new double[nbCf];
for (int loopcf = 0; loopcf < nbCf; loopcf++) {
cfa[loopcf] =
-Math.signum(cfe.getNthPayment(0).getAmount()) * cfe.getNthPayment(loopcf).getAmount();
t[loopcf] = cfe.getNthPayment(loopcf).getPaymentTime();
}
double rhog2pp = g2Data.getG2ppParameter().getCorrelation();
double[][] ht0 = MODEL_G2PP.volatilityMaturityPart(g2Data.getG2ppParameter(), t[0], t);
double[] dfswap = new double[nbCf];
double[] p0 = new double[nbCf];
double[] cP = new double[nbCf];
for (int loopcf = 0; loopcf < nbCf; loopcf++) {
dfswap[loopcf] = dsc.getDiscountFactor(t[loopcf]);
p0[loopcf] = dfswap[loopcf] / dfswap[0];
cP[loopcf] = cfa[loopcf] * p0[loopcf];
}
double k = -cfa[0];
double b0 = 0.0;
for (int loopcf = 1; loopcf < nbCf; loopcf++) {
b0 += cP[loopcf];
}
double[] alpha0 = new double[nbCf - 1];
double[] beta0 = new double[2];
for (int loopcf = 0; loopcf < nbCf - 1; loopcf++) {
alpha0[loopcf] = cfa[loopcf + 1] * p0[loopcf + 1] / b0;
beta0[0] += alpha0[loopcf] * ht0[0][loopcf + 1];
beta0[1] += alpha0[loopcf] * ht0[1][loopcf + 1];
}
double[][] gamma = MODEL_G2PP.gamma(g2Data.getG2ppParameter(), 0, swaption.getTimeToExpiry());
double[] tau = new double[nbCf];
for (int loopcf = 0; loopcf < nbCf; loopcf++) {
tau[loopcf] =
gamma[0][0] * ht0[0][loopcf] * ht0[0][loopcf]
+ gamma[1][1] * ht0[1][loopcf] * ht0[1][loopcf]
+ 2 * rhog2pp * gamma[0][1] * ht0[0][loopcf] * ht0[1][loopcf];
}
double xbarnum = 0.0;
double xbarde = 0.0;
for (int loopcf = 0; loopcf < nbCf; loopcf++) {
xbarnum += cP[loopcf] - cP[loopcf] * tau[loopcf] * tau[loopcf] / 2.0;
xbarde += cP[loopcf] * tau[loopcf];
}
double xbar = xbarnum / xbarde;
double[] pK = new double[nbCf];
for (int loopcf = 0; loopcf < nbCf; loopcf++) {
pK[loopcf] = p0[loopcf] * (1.0 - tau[loopcf] * xbar - tau[loopcf] * tau[loopcf] / 2.0);
}
double[] alphaK = new double[nbCf - 1];
double[] betaK = new double[2];
for (int loopcf = 0; loopcf < nbCf - 1; loopcf++) {
alphaK[loopcf] = cfa[loopcf + 1] * pK[loopcf + 1] / k;
betaK[0] += alphaK[loopcf] * ht0[0][loopcf + 1];
betaK[1] += alphaK[loopcf] * ht0[1][loopcf + 1];
}
double[] betaBar = new double[] {(beta0[0] + betaK[0]) / 2.0, (beta0[1] + betaK[1]) / 2.0};
double sigmaBar2 =
gamma[0][0] * betaBar[0] * betaBar[0]
+ gamma[1][1] * betaBar[1] * betaBar[1]
+ 2 * rhog2pp * gamma[0][1] * betaBar[0] * betaBar[1];
double sigmaBar = Math.sqrt(sigmaBar2);
EuropeanVanillaOption option = new EuropeanVanillaOption(k, 1, !swaption.isCall());
final BlackPriceFunction blackFunction = new BlackPriceFunction();
final BlackFunctionData dataBlack = new BlackFunctionData(b0, dfswap[0], sigmaBar);
final Function1D<BlackFunctionData, Double> func = blackFunction.getPriceFunction(option);
final double price = func.evaluate(dataBlack) * (swaption.isLong() ? 1.0 : -1.0);
return CurrencyAmount.of(swaption.getCurrency(), price);
}
/** {@inheritDoc} */
@Override
public IsdaCompliantCreditCurve calibrateCreditCurve(
CdsAnalytic[] cds,
double[] premiums,
IsdaCompliantYieldCurve yieldCurve,
double[] pointsUpfront) {
ArgChecker.noNulls(cds, "null CDSs");
ArgChecker.notEmpty(premiums, "empty fractionalSpreads");
ArgChecker.notEmpty(pointsUpfront, "empty pointsUpfront");
ArgChecker.notNull(yieldCurve, "null yieldCurve");
int n = cds.length;
ArgChecker.isTrue(n == premiums.length, "Number of CDSs does not match number of spreads");
ArgChecker.isTrue(
n == pointsUpfront.length, "Number of CDSs does not match number of pointsUpfront");
double proStart = cds[0].getEffectiveProtectionStart();
for (int i = 1; i < n; i++) {
ArgChecker.isTrue(
proStart == cds[i].getEffectiveProtectionStart(),
"all CDSs must has same protection start");
ArgChecker.isTrue(
cds[i].getProtectionEnd() > cds[i - 1].getProtectionEnd(),
"protection end must be ascending");
}
// use continuous premiums as initial guess
double[] guess = new double[n];
double[] t = new double[n];
for (int i = 0; i < n; i++) {
t[i] = cds[i].getProtectionEnd();
guess[i] = (premiums[i] + pointsUpfront[i] / t[i]) / cds[i].getLGD();
}
IsdaCompliantCreditCurve creditCurve = new IsdaCompliantCreditCurve(t, guess);
for (int i = 0; i < n; i++) {
Pricer pricer = new Pricer(cds[i], yieldCurve, t, premiums[i], pointsUpfront[i]);
Function1D<Double, Double> func = pricer.getPointFunction(i, creditCurve);
switch (getArbHanding()) {
case Ignore:
{
try {
double[] bracket =
BRACKER.getBracketedPoints(
func,
0.8 * guess[i],
1.25 * guess[i],
Double.NEGATIVE_INFINITY,
Double.POSITIVE_INFINITY);
double zeroRate =
bracket[0] > bracket[1]
? ROOTFINDER.getRoot(func, bracket[1], bracket[0])
: ROOTFINDER.getRoot(func, bracket[0], bracket[1]); // Negative guess handled
creditCurve = creditCurve.withRate(zeroRate, i);
} catch (
MathException e) { // handling bracketing failure due to small survival probability
if (Math.abs(func.evaluate(creditCurve.getZeroRateAtIndex(i - 1))) < 1.e-12) {
creditCurve = creditCurve.withRate(creditCurve.getZeroRateAtIndex(i - 1), i);
} else {
throw new MathException(e);
}
}
break;
}
case Fail:
{
double minValue =
i == 0 ? 0.0 : creditCurve.getRTAtIndex(i - 1) / creditCurve.getTimeAtIndex(i);
if (i > 0 && func.evaluate(minValue) > 0.0) { // can never fail on the first spread
StringBuilder msg = new StringBuilder();
if (pointsUpfront[i] == 0.0) {
msg.append("The par spread of " + premiums[i] + " at index " + i);
} else {
msg.append(
"The premium of "
+ premiums[i]
+ "and points up-front of "
+ pointsUpfront[i]
+ " at index "
+ i);
}
msg.append(
" is an arbitrage; cannot fit a curve with positive forward hazard rate. ");
throw new IllegalArgumentException(msg.toString());
}
guess[i] = Math.max(minValue, guess[i]);
double[] bracket =
BRACKER.getBracketedPoints(
func, guess[i], 1.2 * guess[i], minValue, Double.POSITIVE_INFINITY);
double zeroRate = ROOTFINDER.getRoot(func, bracket[0], bracket[1]);
creditCurve = creditCurve.withRate(zeroRate, i);
break;
}
case ZeroHazardRate:
{
double minValue =
i == 0 ? 0.0 : creditCurve.getRTAtIndex(i - 1) / creditCurve.getTimeAtIndex(i);
if (i > 0 && func.evaluate(minValue) > 0.0) { // can never fail on the first spread
creditCurve = creditCurve.withRate(minValue, i);
} else {
guess[i] = Math.max(minValue, guess[i]);
double[] bracket =
BRACKER.getBracketedPoints(
func, guess[i], 1.2 * guess[i], minValue, Double.POSITIVE_INFINITY);
double zeroRate = ROOTFINDER.getRoot(func, bracket[0], bracket[1]);
creditCurve = creditCurve.withRate(zeroRate, i);
}
break;
}
default:
throw new IllegalArgumentException("unknow case " + getArbHanding());
}
}
return creditCurve;
}
@SuppressWarnings("synthetic-access")
@Override
public Double visitStrike(final BlackVolatilitySurfaceStrike surface) {
final double atmVol = surface.getVolatility(_t, _f);
if (_t < 1e-4) {
return atmVol * atmVol;
}
final double rootT = Math.sqrt(_t);
final double invNorTol = NORMAL.getInverseCDF(_tol);
final Function1D<Double, Double> integrand = getStrikeIntegrand(surface);
final Function1D<Double, Double> remainder =
new Function1D<Double, Double>() {
@Override
public Double evaluate(final Double strike) {
if (strike == 0) {
return 0.0;
}
final boolean isCall = strike >= _f;
final double vol = surface.getVolatility(_t, strike);
final double otmPrice = BlackFormulaRepository.price(_f, strike, _t, vol, isCall);
final double res = (isCall ? otmPrice / strike : otmPrice / 2 / strike);
return res;
}
};
double putPart;
if (_addResidual) {
putPart = _integrator.integrate(integrand, _lowStrikeCutoff, _f);
putPart += _residual;
} else {
double l = _f * Math.exp(invNorTol * atmVol * rootT); // initial estimate of lower limit
putPart = _integrator.integrate(integrand, l, _f);
double rem = remainder.evaluate(l);
double error = rem / putPart;
while (error > _tol) {
l /= 2.0;
putPart += _integrator.integrate(integrand, l, 2 * l);
rem = remainder.evaluate(l);
error = rem / putPart;
}
putPart +=
rem; // add on the (very small) remainder estimate otherwise we'll always underestimate
// variance
}
double u = _f * Math.exp(-invNorTol * atmVol * rootT); // initial estimate of upper limit
double callPart = _integrator.integrate(integrand, _f, u);
double rem = remainder.evaluate(u);
double error = rem / callPart;
while (error > _tol) {
callPart += _integrator.integrate(integrand, u, 2 * u);
u *= 2.0;
rem = remainder.evaluate(u);
error = rem / putPart;
}
// callPart += rem/2.0;
// don't add on the remainder estimate as it is very conservative, and likely too large
return 2 * (putPart + callPart) / _t;
}
@Override
public Double evaluate(final Double lambda) {
final DoubleMatrix1D x = (DoubleMatrix1D) OG_ALGEBRA.add(_x0, OG_ALGEBRA.scale(_p, lambda));
return _f.evaluate(x);
}
/** Check trivial extrapolation is recovered for Benaim-Dodgson-Kainth extrapolation */
@Test
public void functionRecoveryBDKExtrapolationTest() {
double forward = 1.0;
double expiry = 3.0;
int nSamples = 4;
double[] strikes = new double[nSamples];
double[] vols = new double[nSamples];
final double mu = 1.0;
final double a = -1.0;
final double b = 0.0;
final double c = 0.0;
// Expected left extrapolation
Function1D<Double, Double> left =
new Function1D<Double, Double>() {
@Override
public Double evaluate(Double strike) {
return Math.pow(strike, mu) * Math.exp(a + b * strike + c * strike * strike);
}
};
// Expected right extrapolation
Function1D<Double, Double> right =
new Function1D<Double, Double>() {
@Override
public Double evaluate(Double strike) {
return Math.pow(strike, -mu) * Math.exp(a + b / strike + c / strike / strike);
}
};
for (int i = 0; i < nSamples; ++i) {
double strike = forward * (0.75 + 0.05 * i);
double price = left.evaluate(strike);
double vol = BlackFormulaRepository.impliedVolatility(price, forward, strike, expiry, false);
strikes[i] = strike;
vols[i] = vol;
}
SmileExtrapolationFunctionSABRProvider extrapBDK =
new BenaimDodgsonKainthExtrapolationFunctionProvider(mu, mu);
SmileInterpolatorSABRWithExtrapolation interpBDK =
new SmileInterpolatorSABRWithExtrapolation(
new SABRBerestyckiVolatilityFunction(), extrapBDK);
InterpolatedSmileFunction funcBDK =
new InterpolatedSmileFunction(interpBDK, forward, strikes, expiry, vols);
double[] keys = new double[] {forward * 0.1, forward * 0.5, forward * 0.66};
for (int i = 0; i < keys.length; ++i) {
double vol = funcBDK.getVolatility(keys[i]);
double price = BlackFormulaRepository.price(forward, keys[i], expiry, vol, false);
assertEquals(left.evaluate(keys[i]), price, 1.e-2);
}
for (int i = 0; i < nSamples; ++i) {
double strike = forward * (1.1 + 0.05 * i);
double price = right.evaluate(strike);
double vol = BlackFormulaRepository.impliedVolatility(price, forward, strike, expiry, true);
strikes[i] = strike;
vols[i] = vol;
}
extrapBDK = new BenaimDodgsonKainthExtrapolationFunctionProvider(mu, mu);
interpBDK = new SmileInterpolatorSABRWithExtrapolation(extrapBDK);
funcBDK = new InterpolatedSmileFunction(interpBDK, forward, strikes, expiry, vols);
keys = new double[] {forward * 1.31, forward * 1.5, forward * 2.61, forward * 15.0};
for (int i = 0; i < keys.length; ++i) {
double vol = funcBDK.getVolatility(keys[i]);
double price = BlackFormulaRepository.price(forward, keys[i], expiry, vol, true);
assertEquals(right.evaluate(keys[i]), price, 1.e-2);
}
}
@Test
/**
* Tests the 'In-Out Parity' condition: The price of a Knock-In plus a Knock-Out of arbitrary
* barrier level must equal that of the underlying vanilla option + value of the rebate
*/
public void impossibleToHitBarrierIsVanilla() {
final Barrier veryLowKnockIn =
new Barrier(KnockType.IN, BarrierType.DOWN, ObservationType.CONTINUOUS, 1e-6);
final Barrier veryLowKnockOut =
new Barrier(KnockType.OUT, BarrierType.DOWN, ObservationType.CONTINUOUS, 1e-6);
final Barrier veryHighKnockIn =
new Barrier(KnockType.IN, BarrierType.UP, ObservationType.CONTINUOUS, 1e6);
final Barrier veryHighKnockOut =
new Barrier(KnockType.OUT, BarrierType.UP, ObservationType.CONTINUOUS, 1e6);
final double pxRebate = DF_DOM * REBATE;
final Function1D<BlackFunctionData, Double> fcnVanillaCall =
BLACK_FUNCTION.getPriceFunction(VANILLA_CALL_K100);
final double pxVanillaCall = fcnVanillaCall.evaluate(DATA_BLACK);
// KnockIn's with impossible to reach barrier's are guaranteed to pay the rebate at maturity
final double pxDownInPut =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_K100, veryLowKnockIn, REBATE, SPOT, COST_OF_CARRY, RATE_DOM, VOLATILITY);
assertTrue("VeryLowKnockInBarrier doesn't match rebate", pxDownInPut / pxRebate - 1 < 1e-6);
final double pxDownInCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, veryLowKnockIn, REBATE, SPOT, COST_OF_CARRY, RATE_DOM, VOLATILITY);
assertTrue("VeryLowKnockInBarrier doesn't match rebate", pxDownInCall / pxRebate - 1 < 1e-6);
final double pxUpInCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, veryHighKnockIn, REBATE, SPOT, COST_OF_CARRY, RATE_DOM, VOLATILITY);
assertTrue("VeryHighKnockInBarrier doesn't match rebate", pxUpInCall / pxRebate - 1 < 1e-6);
// KnockOut's with impossible to reach barrier's are guaranteed to pay the value of the
// underlying vanilla
final double pxDownOutCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, veryLowKnockOut, REBATE, SPOT, COST_OF_CARRY, RATE_DOM, VOLATILITY);
assertTrue(
"VeryLowKnockInBarrier doesn't match rebate",
Math.abs(pxDownOutCall / pxVanillaCall - 1) < 1e-6);
// Derivatives
final double[] derivs = new double[5];
BARRIER_FUNCTION.getPriceAdjoint(
VANILLA_CALL_K100,
veryLowKnockIn,
REBATE,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY,
derivs);
assertTrue(
"Impossible KnockIn: rate sens is incorrect",
derivs[2] / Math.abs((-1 * EXPIRY_TIME * DF_DOM * REBATE) - 1) < 1e-6);
assertEquals(
"Impossible KnockIn: Encountered derivative, other than d/dr, != 0",
0.0,
derivs[0] + derivs[1] + derivs[3] + derivs[4],
1.0e-6);
BARRIER_FUNCTION.getPriceAdjoint(
VANILLA_CALL_K100,
veryHighKnockIn,
REBATE,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY,
derivs);
assertTrue(
"Impossible KnockIn: rate sens is incorrect",
derivs[2] / Math.abs((-1 * EXPIRY_TIME * DF_DOM * REBATE) - 1) < 1e-6);
assertEquals(
"Impossible KnockIn: Encountered derivative, other than d/dr, != 0",
0.0,
derivs[0] + derivs[1] + derivs[3] + derivs[4],
1.0e-6);
// Barrier: [0] spot, [1] strike, [2] rate, [3] cost-of-carry, [4] volatility.
BARRIER_FUNCTION.getPriceAdjoint(
VANILLA_CALL_K100,
veryLowKnockOut,
REBATE,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY,
derivs);
// Vanilla: [0] the price, [1] the derivative with respect to the forward, [2] the derivative
// with respect to the volatility and [3] the derivative with respect to the strike.
final double[] vanillaDerivs = BLACK_FUNCTION.getPriceAdjoint(VANILLA_CALL_K100, DATA_BLACK);
assertEquals(
"Impossible KnockOut: Vega doesn't match vanilla", vanillaDerivs[2], derivs[4], 1e-6);
assertEquals(
"Impossible KnockOut: Dual Delta (d/dK) doesn't match vanilla",
vanillaDerivs[3],
derivs[1],
1e-6);
assertEquals(
"Impossible KnockOut: Delta doesn't match vanilla",
vanillaDerivs[1] * DF_FOR / DF_DOM,
derivs[0],
1e-6);
BARRIER_FUNCTION.getPriceAdjoint(
VANILLA_CALL_K100,
veryHighKnockOut,
REBATE,
SPOT,
COST_OF_CARRY,
RATE_DOM,
VOLATILITY,
derivs);
assertEquals(
"Impossible KnockOut: Vega doesn't match vanilla", vanillaDerivs[2], derivs[4], 1e-6);
assertEquals(
"Impossible KnockOut: Dual Delta (d/dK) doesn't match vanilla",
vanillaDerivs[3],
derivs[1],
1e-6);
assertEquals(
"Impossible KnockOut: Delta doesn't match vanilla",
vanillaDerivs[1] * DF_FOR / DF_DOM,
derivs[0],
1e-6);
}
@Test
/**
* Tests the 'In-Out Parity' condition: The price of a Knock-In plus a Knock-Out of arbitrary
* barrier level must equal that of the underlying vanilla option + value of the rebate
*/
public void inOutParityMorePathsWithRebate() {
// Market with zero rates, domestic and foreign
final BlackFunctionData zeroRatesMarket = new BlackFunctionData(SPOT, 1.0, VOLATILITY);
final double rateDomestic = 0.0;
final double rateForeign = 0.0;
final double costOfCarry = rateDomestic - rateForeign;
// Rebate
final double pxRebate = REBATE;
// 2 - Vanillas - Call and Put
final Function1D<BlackFunctionData, Double> fcnVanillaCall =
BLACK_FUNCTION.getPriceFunction(VANILLA_CALL_K100);
final double pxVanillaCall = fcnVanillaCall.evaluate(zeroRatesMarket);
final Function1D<BlackFunctionData, Double> fcnVanillaPut =
BLACK_FUNCTION.getPriceFunction(VANILLA_PUT_K100);
final double pxVanillaPut = fcnVanillaPut.evaluate(zeroRatesMarket);
// Barriers: Up and Down, Call and Put, In and Out
final double pxDownInCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100,
BARRIER_DOWN_IN,
REBATE,
SPOT,
costOfCarry,
rateDomestic,
VOLATILITY);
final double pxDownOutCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100,
BARRIER_DOWN_OUT,
REBATE,
SPOT,
costOfCarry,
rateDomestic,
VOLATILITY);
assertEquals(
"Knock In-Out Parity fails",
1.0,
(pxVanillaCall + pxRebate) / (pxDownInCall + pxDownOutCall),
1.e-6);
// assertTrue("Knock In-Out Parity fails", Math.abs((pxVanillaCall + pxRebate) / (pxDownInCall +
// pxDownOutCall) - 1) < 1.e-6);
final double pxDownInPut =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_K100, BARRIER_DOWN_IN, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
final double pxDownOutPut =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_K100,
BARRIER_DOWN_OUT,
REBATE,
SPOT,
costOfCarry,
rateDomestic,
VOLATILITY);
assertTrue(
"Knock In-Out Parity fails",
Math.abs((pxVanillaPut + pxRebate) / (pxDownInPut + pxDownOutPut) - 1) < 1.e-6);
final double pxUpInCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, BARRIER_UP_IN, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
final double pxUpOutCall =
BARRIER_FUNCTION.getPrice(
VANILLA_CALL_K100, BARRIER_UP_OUT, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
assertTrue(
"Knock In-Out Parity fails",
Math.abs((pxVanillaCall + pxRebate) / (pxUpInCall + pxUpOutCall) - 1) < 1.e-6);
final double pxUpInPut =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_K100, BARRIER_UP_IN, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
final double pxUpOutPut =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_K100, BARRIER_UP_OUT, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
assertTrue(
"Knock In-Out Parity fails",
Math.abs((pxVanillaPut + pxRebate) / (pxUpInPut + pxUpOutPut) - 1) < 1.e-6);
// Let's try the Up case with Barrier < Strike. To do this, I create a new vanilla with K120 (>
// Barrier110)
final Function1D<BlackFunctionData, Double> fcnVanillaPutHiK =
BLACK_FUNCTION.getPriceFunction(VANILLA_PUT_KHI);
final double pxVanillaPutHiK = fcnVanillaPutHiK.evaluate(zeroRatesMarket);
final double pxUpInPutHiK =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_KHI, BARRIER_UP_IN, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
final double pxUpOutPutHiK =
BARRIER_FUNCTION.getPrice(
VANILLA_PUT_KHI, BARRIER_UP_OUT, REBATE, SPOT, costOfCarry, rateDomestic, VOLATILITY);
assertTrue(
"Knock In-Out Parity fails",
Math.abs((pxVanillaPutHiK + pxRebate) / (pxUpInPutHiK + pxUpOutPutHiK) - 1) < 1.e-6);
}
@Override
public Set<ComputedValue> execute(
final FunctionExecutionContext executionContext,
final FunctionInputs inputs,
final ComputationTarget target,
final Set<ValueRequirement> desiredValues)
throws AsynchronousExecution {
final Object originalCurveObject = inputs.getValue(YIELD_CURVE);
if (originalCurveObject == null) {
throw new OpenGammaRuntimeException("Could not get original curve");
}
ValueProperties resultCurveProperties = null;
String absoluteToleranceName = null;
String relativeToleranceName = null;
String iterationsName = null;
String decompositionName = null;
String useFiniteDifferenceName = null;
for (final ValueRequirement desiredValue : desiredValues) {
if (desiredValue.getValueName().equals(YIELD_CURVE)) {
absoluteToleranceName =
desiredValue.getConstraint(
MultiYieldCurvePropertiesAndDefaults.PROPERTY_ROOT_FINDER_ABSOLUTE_TOLERANCE);
relativeToleranceName =
desiredValue.getConstraint(
MultiYieldCurvePropertiesAndDefaults.PROPERTY_ROOT_FINDER_RELATIVE_TOLERANCE);
iterationsName =
desiredValue.getConstraint(
MultiYieldCurvePropertiesAndDefaults.PROPERTY_ROOT_FINDER_MAX_ITERATIONS);
decompositionName =
desiredValue.getConstraint(
MultiYieldCurvePropertiesAndDefaults.PROPERTY_DECOMPOSITION);
useFiniteDifferenceName =
desiredValue.getConstraint(
MultiYieldCurvePropertiesAndDefaults.PROPERTY_USE_FINITE_DIFFERENCE);
resultCurveProperties = desiredValue.getConstraints().copy().get();
break;
}
}
if (resultCurveProperties == null) {
throw new OpenGammaRuntimeException("Could not get result curve properties");
}
final ValueProperties resultJacobianProperties = resultCurveProperties.withoutAny(CURVE);
ZonedDateTime valuationDateTime =
executionContext
.getValuationTime()
.atZone(executionContext.getValuationClock().getZone());
final HolidaySource holidaySource =
OpenGammaExecutionContext.getHolidaySource(executionContext);
final ConventionSource conventionSource =
OpenGammaExecutionContext.getConventionSource(executionContext);
final Calendar calendar = CalendarUtils.getCalendar(holidaySource, _currency);
final DepositConvention convention =
conventionSource.getSingle(
ExternalId.of(SCHEME_NAME, getConventionName(_currency, DEPOSIT)),
DepositConvention.class);
final int spotLag = convention.getSettlementDays();
final ExternalId conventionSettlementRegion = convention.getRegionCalendar();
ZonedDateTime spotDate;
if (spotLag == 0 && conventionSettlementRegion == null) {
spotDate = valuationDateTime;
} else {
spotDate = ScheduleCalculator.getAdjustedDate(valuationDateTime, spotLag, calendar);
;
}
final YieldCurveBundle curves = new YieldCurveBundle();
final String fullYieldCurveName = _originalCurveName + "_" + _currency;
curves.setCurve(fullYieldCurveName, (YieldAndDiscountCurve) originalCurveObject);
final int n = _impliedDefinition.getStrips().size();
final double[] t = new double[n];
final double[] r = new double[n];
int i = 0;
final DayCount dayCount =
DayCountFactory.INSTANCE.getDayCount(
"Act/360"); // TODO: Get the convention from the curve.
final String impliedDepositCurveName = _curveCalculationConfig + "_" + _currency.getCode();
final List<InstrumentDerivative> derivatives = new ArrayList<>();
for (final FixedIncomeStrip strip : _impliedDefinition.getStrips()) {
final Tenor tenor = strip.getCurveNodePointTime();
final ZonedDateTime paymentDate =
ScheduleCalculator.getAdjustedDate(
spotDate, tenor.getPeriod(), MOD_FOL, calendar, true);
final double startTime = TimeCalculator.getTimeBetween(valuationDateTime, spotDate);
final double endTime = TimeCalculator.getTimeBetween(valuationDateTime, paymentDate);
final double accrualFactor = dayCount.getDayCountFraction(spotDate, paymentDate, calendar);
final Cash cashFXCurve =
new Cash(_currency, startTime, endTime, 1, 0, accrualFactor, fullYieldCurveName);
final double parRate = METHOD_CASH.parRate(cashFXCurve, curves);
final Cash cashDepositCurve =
new Cash(_currency, startTime, endTime, 1, 0, accrualFactor, impliedDepositCurveName);
derivatives.add(cashDepositCurve);
t[i] = endTime;
r[i++] = parRate;
}
final CombinedInterpolatorExtrapolator interpolator =
CombinedInterpolatorExtrapolatorFactory.getInterpolator(
_interpolatorName, _leftExtrapolatorName, _rightExtrapolatorName);
final double absoluteTolerance = Double.parseDouble(absoluteToleranceName);
final double relativeTolerance = Double.parseDouble(relativeToleranceName);
final int iterations = Integer.parseInt(iterationsName);
final Decomposition<?> decomposition =
DecompositionFactory.getDecomposition(decompositionName);
final boolean useFiniteDifference = Boolean.parseBoolean(useFiniteDifferenceName);
final LinkedHashMap<String, double[]> curveNodes = new LinkedHashMap<>();
final LinkedHashMap<String, Interpolator1D> interpolators = new LinkedHashMap<>();
curveNodes.put(impliedDepositCurveName, t);
interpolators.put(impliedDepositCurveName, interpolator);
final FXMatrix fxMatrix = new FXMatrix();
final YieldCurveBundle knownCurve = new YieldCurveBundle();
final MultipleYieldCurveFinderDataBundle data =
new MultipleYieldCurveFinderDataBundle(
derivatives, r, knownCurve, curveNodes, interpolators, useFiniteDifference, fxMatrix);
final NewtonVectorRootFinder rootFinder =
new BroydenVectorRootFinder(
absoluteTolerance, relativeTolerance, iterations, decomposition);
final Function1D<DoubleMatrix1D, DoubleMatrix1D> curveCalculator =
new MultipleYieldCurveFinderFunction(data, PAR_RATE_CALCULATOR);
final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator =
new MultipleYieldCurveFinderJacobian(data, PAR_RATE_SENSITIVITY_CALCULATOR);
final double[] fittedYields =
rootFinder.getRoot(curveCalculator, jacobianCalculator, new DoubleMatrix1D(r)).getData();
final DoubleMatrix2D jacobianMatrix =
jacobianCalculator.evaluate(new DoubleMatrix1D(fittedYields));
final YieldCurve impliedDepositCurve =
new YieldCurve(
impliedDepositCurveName,
InterpolatedDoublesCurve.from(t, fittedYields, interpolator));
final ValueSpecification curveSpec =
new ValueSpecification(YIELD_CURVE, target.toSpecification(), resultCurveProperties);
final ValueSpecification jacobianSpec =
new ValueSpecification(
YIELD_CURVE_JACOBIAN, target.toSpecification(), resultJacobianProperties);
return Sets.newHashSet(
new ComputedValue(curveSpec, impliedDepositCurve),
new ComputedValue(jacobianSpec, jacobianMatrix));
}