//-------------------------------------------------------------------------
/// <summary>
/// Calculates the market quote sensitivities from parameter sensitivity.
/// </summary>
/// <param name="paramSensitivities"> the curve parameter sensitivities </param>
/// <param name="provider"> the rates provider, containing Jacobian calibration information </param>
/// <returns> the market quote sensitivities </returns>
public virtual CurrencyParameterSensitivities sensitivity(CurrencyParameterSensitivities paramSensitivities, RatesProvider provider)
{
ArgChecker.notNull(paramSensitivities, "paramSensitivities");
ArgChecker.notNull(provider, "provider");
CurrencyParameterSensitivities result = CurrencyParameterSensitivities.empty();
foreach (CurrencyParameterSensitivity paramSens in paramSensitivities.Sensitivities)
{
// find the matching calibration info
Curve curve = provider.findData(paramSens.MarketDataName).filter(v => v is Curve).map(v => (Curve)v).orElseThrow(() => new System.ArgumentException("Market Quote sensitivity requires curve: " + paramSens.MarketDataName));
JacobianCalibrationMatrix info = curve.Metadata.findInfo(CurveInfoType.JACOBIAN).orElseThrow(() => new System.ArgumentException("Market Quote sensitivity requires Jacobian calibration information"));
// calculate the market quote sensitivity using the Jacobian
DoubleMatrix jacobian = info.JacobianMatrix;
DoubleArray paramSensMatrix = paramSens.Sensitivity;
DoubleArray marketQuoteSensMatrix = (DoubleArray)MATRIX_ALGEBRA.multiply(paramSensMatrix, jacobian);
DoubleArray marketQuoteSens = marketQuoteSensMatrix;
// split between different curves
IDictionary <CurveName, DoubleArray> split = info.splitValues(marketQuoteSens);
foreach (KeyValuePair <CurveName, DoubleArray> entry in split.SetOfKeyValuePairs())
{
CurveName curveName = entry.Key;
CurrencyParameterSensitivity maketQuoteSens = provider.findData(curveName).map(c => c.createParameterSensitivity(paramSens.Currency, entry.Value)).orElse(CurrencyParameterSensitivity.of(curveName, paramSens.Currency, entry.Value));
result = result.combinedWith(maketQuoteSens);
}
}
return(result);
}
/// <summary>
/// get the k^th order penalty matrix, P. This is defined as P = (D^T)*D , where D is the k^th order difference
/// matrix (see getDifferenceMatrix), so the scalar amount (x^T)*P*x = |Dx|^2 is greater the more k^th order
/// differences there are in the vector, x. This can then act as a penalty on x in some optimisation routine where
/// x is the vector of (fit) parameters. </summary>
/// <param name="m"> Length of the vector. </param>
/// <param name="k"> Difference order. Require m > k </param>
/// <returns> The k^th order penalty matrix, P </returns>
public static DoubleMatrix getPenaltyMatrix(int m, int k)
{
ArgChecker.notNegativeOrZero(m, "m");
ArgChecker.notNegative(k, "k");
ArgChecker.isTrue(k < m, "Difference order too high, require m > k, but have: m = {} and k = {}", m, k);
if (k == 0)
{
return(DoubleMatrix.identity(m));
}
DoubleMatrix d = getDifferenceMatrix(m, k);
DoubleMatrix dt = MA.getTranspose(d);
return((DoubleMatrix)MA.multiply(dt, d));
}
// jacobian indirect, merging groups
private static DoubleMatrix jacobianIndirect(DoubleMatrix res, DoubleMatrix pDmCurrentMatrix, int nbTrades, int totalParamsGroup, int totalParamsPrevious, ImmutableList <CurveParameterSize> orderPrevious, ImmutableMap <CurveName, JacobianCalibrationMatrix> jacobiansPrevious)
{
if (totalParamsPrevious == 0)
{
return(DoubleMatrix.EMPTY);
}
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] nonDirect = new double[totalParamsGroup][totalParamsPrevious];
double[][] nonDirect = RectangularArrays.ReturnRectangularDoubleArray(totalParamsGroup, totalParamsPrevious);
for (int i = 0; i < nbTrades; i++)
{
Array.Copy(res.rowArray(i), 0, nonDirect[i], 0, totalParamsPrevious);
}
DoubleMatrix pDpPreviousMatrix = (DoubleMatrix)MATRIX_ALGEBRA.scale(MATRIX_ALGEBRA.multiply(pDmCurrentMatrix, DoubleMatrix.copyOf(nonDirect)), -1d);
// all curves: order and size
int[] startIndexBefore = new int[orderPrevious.size()];
for (int i = 1; i < orderPrevious.size(); i++)
{
startIndexBefore[i] = startIndexBefore[i - 1] + orderPrevious.get(i - 1).ParameterCount;
}
// transition Matrix: all curves from previous groups
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] transition = new double[totalParamsPrevious][totalParamsPrevious];
double[][] transition = RectangularArrays.ReturnRectangularDoubleArray(totalParamsPrevious, totalParamsPrevious);
for (int i = 0; i < orderPrevious.size(); i++)
{
int paramCountOuter = orderPrevious.get(i).ParameterCount;
JacobianCalibrationMatrix thisInfo = jacobiansPrevious.get(orderPrevious.get(i).Name);
DoubleMatrix thisMatrix = thisInfo.JacobianMatrix;
int startIndexInner = 0;
for (int j = 0; j < orderPrevious.size(); j++)
{
int paramCountInner = orderPrevious.get(j).ParameterCount;
if (thisInfo.containsCurve(orderPrevious.get(j).Name))
{ // If not, the matrix stay with 0
for (int k = 0; k < paramCountOuter; k++)
{
Array.Copy(thisMatrix.rowArray(k), startIndexInner, transition[startIndexBefore[i] + k], startIndexBefore[j], paramCountInner);
}
}
startIndexInner += paramCountInner;
}
}
DoubleMatrix transitionMatrix = DoubleMatrix.copyOf(transition);
return((DoubleMatrix)MATRIX_ALGEBRA.multiply(pDpPreviousMatrix, transitionMatrix));
}
private DoubleMatrix getDiffMatrix(int m, int k)
{
ArgChecker.isTrue(k < m, "difference order too high");
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] data = new double[m][m];
double[][] data = RectangularArrays.ReturnRectangularDoubleArray(m, m);
if (m == 0)
{
return(DoubleMatrix.copyOf(data));
}
int[] coeff = new int[k + 1];
int sign = 1;
for (int i = k; i >= 0; i--)
{
coeff[i] = (int)(sign * binomialCoefficient(k, i));
sign *= -1;
}
for (int i = k; i < m; i++)
{
for (int j = 0; j < k + 1; j++)
{
data[i][j + i - k] = coeff[j];
}
}
DoubleMatrix d = DoubleMatrix.copyOf(data);
DoubleMatrix dt = _algebra.getTranspose(d);
return((DoubleMatrix)_algebra.multiply(dt, d));
}
//-------------------------------------------------------------------------
// shift the curve
private NodalCurve withShift(InterpolatedNodalCurve curve, IList <ParameterMetadata> parameterMetadata, DoubleMatrix sensitivity, bool computeJacobian, double shift)
{
int nNode = curve.ParameterCount;
if (shift < curve.XValues.get(0))
{
//offset less than t value of 1st knot, so no knots are not removed
double eta = curve.YValues.get(0) * shift;
DoubleArray time = DoubleArray.of(nNode, i => curve.XValues.get(i) - shift);
DoubleArray rate = DoubleArray.of(nNode, i => (curve.YValues.get(i) * curve.XValues.get(i) - eta) / time.get(i));
CurveMetadata metadata = curve.Metadata.withParameterMetadata(parameterMetadata);
if (computeJacobian)
{
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] transf = new double[nNode][nNode];
double[][] transf = RectangularArrays.ReturnRectangularDoubleArray(nNode, nNode);
for (int i = 0; i < nNode; ++i)
{
transf[i][0] = -shift / time.get(i);
transf[i][i] += curve.XValues.get(i) / time.get(i);
}
DoubleMatrix jacobianMatrix = (DoubleMatrix)MATRIX_ALGEBRA.multiply(DoubleMatrix.ofUnsafe(transf), MATRIX_ALGEBRA.getInverse(sensitivity));
JacobianCalibrationMatrix jacobian = JacobianCalibrationMatrix.of(ImmutableList.of(CurveParameterSize.of(curve.Name, nNode)), jacobianMatrix);
return(curve.withValues(time, rate).withMetadata(metadata.withInfo(CurveInfoType.JACOBIAN, jacobian)));
}
return(curve.withValues(time, rate).withMetadata(metadata));
}
if (shift >= curve.XValues.get(nNode - 1))
{
//new base after last knot. The new 'curve' has a constant zero rate which we represent with a nominal knot at 1.0
double time = 1d;
double interval = curve.XValues.get(nNode - 1) - curve.XValues.get(nNode - 2);
double rate = (curve.YValues.get(nNode - 1) * curve.XValues.get(nNode - 1) - curve.YValues.get(nNode - 2) * curve.XValues.get(nNode - 2)) / interval;
if (computeJacobian)
{
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] transf = new double[1][nNode];
double[][] transf = RectangularArrays.ReturnRectangularDoubleArray(1, nNode);
transf[0][nNode - 2] = -curve.XValues.get(nNode - 2) / interval;
transf[0][nNode - 1] = curve.XValues.get(nNode - 1) / interval;
DoubleMatrix jacobianMatrix = (DoubleMatrix)MATRIX_ALGEBRA.multiply(DoubleMatrix.ofUnsafe(transf), MATRIX_ALGEBRA.getInverse(sensitivity));
JacobianCalibrationMatrix jacobian = JacobianCalibrationMatrix.of(ImmutableList.of(CurveParameterSize.of(curve.Name, nNode)), jacobianMatrix);
return(ConstantNodalCurve.of(curve.Metadata.withInfo(CurveInfoType.JACOBIAN, jacobian), time, rate));
}
return(ConstantNodalCurve.of(curve.Metadata, time, rate));
}
//offset greater than (or equal to) t value of 1st knot, so at least one knot must be removed
int index = Arrays.binarySearch(curve.XValues.toArray(), shift);
if (index < 0)
{
index = -(index + 1);
}
else
{
index++;
}
double interval = curve.XValues.get(index) - curve.XValues.get(index - 1);
double tt1 = curve.XValues.get(index - 1) * (curve.XValues.get(index) - shift);
double tt2 = curve.XValues.get(index) * (shift - curve.XValues.get(index - 1));
double eta = (curve.YValues.get(index - 1) * tt1 + curve.YValues.get(index) * tt2) / interval;
int m = nNode - index;
CurveMetadata metadata = curve.Metadata.withParameterMetadata(parameterMetadata.subList(index, nNode));
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int indexFinal = index;
int indexFinal = index;
DoubleArray time = DoubleArray.of(m, i => curve.XValues.get(i + indexFinal) - shift);
DoubleArray rate = DoubleArray.of(m, i => (curve.YValues.get(i + indexFinal) * curve.XValues.get(i + indexFinal) - eta) / time.get(i));
if (computeJacobian)
{
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] transf = new double[m][nNode];
double[][] transf = RectangularArrays.ReturnRectangularDoubleArray(m, nNode);
for (int i = 0; i < m; ++i)
{
transf[i][index - 1] -= tt1 / (time.get(i) * interval);
transf[i][index] -= tt2 / (time.get(i) * interval);
transf[i][i + index] += curve.XValues.get(i + index) / time.get(i);
}
DoubleMatrix jacobianMatrix = (DoubleMatrix)MATRIX_ALGEBRA.multiply(DoubleMatrix.ofUnsafe(transf), MATRIX_ALGEBRA.getInverse(sensitivity));
JacobianCalibrationMatrix jacobian = JacobianCalibrationMatrix.of(ImmutableList.of(CurveParameterSize.of(curve.Name, nNode)), jacobianMatrix);
return(curve.withValues(time, rate).withMetadata(metadata.withInfo(CurveInfoType.JACOBIAN, jacobian)));
}
return(curve.withValues(time, rate).withMetadata(metadata));
}
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @Test public void differenceMatrix1DTest()
public virtual void differenceMatrix1DTest()
{
int n = 7;
DoubleMatrix d0 = PenaltyMatrixGenerator.getDifferenceMatrix(n, 0); //zeroth order
AssertMatrix.assertEqualsMatrix(DoubleMatrix.identity(n), d0, 1e-15);
DoubleArray zeroVector = DoubleArray.filled(n);
DoubleMatrix d1 = PenaltyMatrixGenerator.getDifferenceMatrix(n, 1); //first order difference matrix
assertEquals(n, d1.rowCount());
assertEquals(n, d1.columnCount());
AssertMatrix.assertEqualsVectors(zeroVector, d1.row(0), 1e-15); //first row should be zero
DoubleArray x = DoubleArray.filled(n, 1.0);
DoubleArray d1x = (DoubleArray)MA.multiply(d1, x);
//a constant vector should have zero first order differences
AssertMatrix.assertEqualsVectors(zeroVector, d1x, 1e-14);
DoubleMatrix d2 = PenaltyMatrixGenerator.getDifferenceMatrix(n, 2); //second order difference matrix
assertEquals(n, d2.rowCount());
assertEquals(n, d2.columnCount());
AssertMatrix.assertEqualsVectors(zeroVector, d2.row(0), 1e-15); //first two rows should be zero
AssertMatrix.assertEqualsVectors(zeroVector, d2.row(1), 1e-15);
DoubleArray x2 = DoubleArray.of(n, i => i);
d1x = (DoubleArray)MA.multiply(d1, x2);
//first element of the diff vector is set to zero
DoubleArray ones = DoubleArray.filled(n, 1.0).with(0, 0);
//vector with differences of one
AssertMatrix.assertEqualsVectors(ones, d1x, 1e-14);
DoubleArray d2x = (DoubleArray)MA.multiply(d2, x2);
//a linear vector should have zero second order differences
AssertMatrix.assertEqualsVectors(zeroVector, d2x, 1e-14);
DoubleMatrix d3 = PenaltyMatrixGenerator.getDifferenceMatrix(n, 3); //third order difference matrix
assertEquals(n, d3.rowCount());
assertEquals(n, d3.columnCount());
AssertMatrix.assertEqualsVectors(zeroVector, d3.row(0), 1e-15); //first three rows should be zero
AssertMatrix.assertEqualsVectors(zeroVector, d3.row(1), 1e-15);
AssertMatrix.assertEqualsVectors(zeroVector, d3.row(2), 1e-15);
DoubleArray x3 = DoubleArray.of(n, i => 0.5 + i + 0.1 * i * i);
d1x = (DoubleArray)MA.multiply(d1, x3);
// expected first order diff, first element is zero
DoubleArray exp = DoubleArray.of(n, i => 0.9 + 0.2 * i).with(0, 0);
AssertMatrix.assertEqualsVectors(exp, d1x, 1e-14);
// expected second order diff, first two elements are zero
exp = DoubleArray.filled(n, 0.2).with(0, 0).with(1, 0);
d2x = (DoubleArray)MA.multiply(d2, x3);
AssertMatrix.assertEqualsVectors(exp, d2x, 1e-14);
DoubleArray d3x = (DoubleArray)MA.multiply(d3, x3);
//a quadratic vector should have zero third order differences
AssertMatrix.assertEqualsVectors(zeroVector, d3x, 1e-14);
}
internal virtual LegalEntitySurvivalProbabilities calibrate(IList <CdsIsdaCreditCurveNode> curveNodes, CurveName name, MarketData marketData, ImmutableCreditRatesProvider ratesProvider, DayCount definitionDayCount, Currency definitionCurrency, bool computeJacobian, bool storeTrade, ReferenceData refData)
{
//JAVA TO C# CONVERTER TODO TASK: Method reference arbitrary object instance method syntax is not converted by Java to C# Converter:
//JAVA TO C# CONVERTER TODO TASK: Most Java stream collectors are not converted by Java to C# Converter:
IEnumerator <StandardId> legalEntities = curveNodes.Select(CdsIsdaCreditCurveNode::getLegalEntityId).collect(Collectors.toSet()).GetEnumerator();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
StandardId legalEntityId = legalEntities.next();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
ArgChecker.isFalse(legalEntities.hasNext(), "legal entity must be common to curve nodes");
//JAVA TO C# CONVERTER TODO TASK: Most Java stream collectors are not converted by Java to C# Converter:
IEnumerator <Currency> currencies = curveNodes.Select(n => n.Template.Convention.Currency).collect(Collectors.toSet()).GetEnumerator();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
Currency currency = currencies.next();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
ArgChecker.isFalse(currencies.hasNext(), "currency must be common to curve nodes");
ArgChecker.isTrue(definitionCurrency.Equals(currency), "curve definition currency must be the same as the currency of CDS");
//JAVA TO C# CONVERTER TODO TASK: Most Java stream collectors are not converted by Java to C# Converter:
IEnumerator <CdsQuoteConvention> quoteConventions = curveNodes.Select(n => n.QuoteConvention).collect(Collectors.toSet()).GetEnumerator();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
CdsQuoteConvention quoteConvention = quoteConventions.next();
//JAVA TO C# CONVERTER TODO TASK: Java iterators are only converted within the context of 'while' and 'for' loops:
ArgChecker.isFalse(quoteConventions.hasNext(), "quote convention must be common to curve nodes");
LocalDate valuationDate = marketData.ValuationDate;
ArgChecker.isTrue(valuationDate.Equals(marketData.ValuationDate), "ratesProvider and marketDate must be based on the same valuation date");
CreditDiscountFactors discountFactors = ratesProvider.discountFactors(currency);
ArgChecker.isTrue(definitionDayCount.Equals(discountFactors.DayCount), "credit curve and discount curve must be based on the same day count convention");
RecoveryRates recoveryRates = ratesProvider.recoveryRates(legalEntityId);
int nNodes = curveNodes.Count;
double[] coupons = new double[nNodes];
double[] pufs = new double[nNodes];
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] diag = new double[nNodes][nNodes];
double[][] diag = RectangularArrays.ReturnRectangularDoubleArray(nNodes, nNodes);
ImmutableList.Builder <ResolvedCdsTrade> tradesBuilder = ImmutableList.builder();
for (int i = 0; i < nNodes; i++)
{
CdsCalibrationTrade tradeCalibration = curveNodes[i].trade(1d, marketData, refData);
ResolvedCdsTrade trade = tradeCalibration.UnderlyingTrade.resolve(refData);
tradesBuilder.add(trade);
double[] temp = getStandardQuoteForm(trade, tradeCalibration.Quote, valuationDate, discountFactors, recoveryRates, computeJacobian, refData);
coupons[i] = temp[0];
pufs[i] = temp[1];
diag[i][i] = temp[2];
}
ImmutableList <ResolvedCdsTrade> trades = tradesBuilder.build();
NodalCurve nodalCurve = calibrate(trades, DoubleArray.ofUnsafe(coupons), DoubleArray.ofUnsafe(pufs), name, valuationDate, discountFactors, recoveryRates, refData);
if (computeJacobian)
{
LegalEntitySurvivalProbabilities creditCurve = LegalEntitySurvivalProbabilities.of(legalEntityId, IsdaCreditDiscountFactors.of(currency, valuationDate, nodalCurve));
ImmutableCreditRatesProvider ratesProviderNew = ratesProvider.toBuilder().creditCurves(ImmutableMap.of(Pair.of(legalEntityId, currency), creditCurve)).build();
System.Func <ResolvedCdsTrade, DoubleArray> sensiFunc = quoteConvention.Equals(CdsQuoteConvention.PAR_SPREAD) ? getParSpreadSensitivityFunction(ratesProviderNew, name, currency, refData) : getPointsUpfrontSensitivityFunction(ratesProviderNew, name, currency, refData);
DoubleMatrix sensi = DoubleMatrix.ofArrayObjects(nNodes, nNodes, i => sensiFunc(trades.get(i)));
sensi = (DoubleMatrix)MATRIX_ALGEBRA.multiply(DoubleMatrix.ofUnsafe(diag), sensi);
JacobianCalibrationMatrix jacobian = JacobianCalibrationMatrix.of(ImmutableList.of(CurveParameterSize.of(name, nNodes)), MATRIX_ALGEBRA.getInverse(sensi));
nodalCurve = nodalCurve.withMetadata(nodalCurve.Metadata.withInfo(CurveInfoType.JACOBIAN, jacobian));
}
ImmutableList <ParameterMetadata> parameterMetadata;
if (storeTrade)
{
parameterMetadata = IntStream.range(0, nNodes).mapToObj(n => ResolvedTradeParameterMetadata.of(trades.get(n), curveNodes[n].Label)).collect(Guavate.toImmutableList());
}
else
{
parameterMetadata = IntStream.range(0, nNodes).mapToObj(n => curveNodes[n].metadata(trades.get(n).Product.ProtectionEndDate)).collect(Guavate.toImmutableList());
}
nodalCurve = nodalCurve.withMetadata(nodalCurve.Metadata.withParameterMetadata(parameterMetadata));
return(LegalEntitySurvivalProbabilities.of(legalEntityId, IsdaCreditDiscountFactors.of(currency, valuationDate, nodalCurve)));
}
