/// <summary>
/// Creates an instance specifying the value of eps.
/// </summary>
/// <param name="eps"> the step size used to approximate the derivative </param>
public MatrixFieldFirstOrderDifferentiator(double eps)
{
ArgChecker.isTrue(eps > 1e-15, "eps of {} is below machine tolerance of 1e-15. Please choose a higher value", eps);
this.eps = eps;
this.twoEps = 2 * eps;
this.oneOverTwpEps = 1.0 / twoEps;
}
/// <summary>
/// Calculates polynomials and derivative. </summary>
/// <param name="n"> the n value </param>
/// <param name="alpha"> the alpha value </param>
/// <param name="beta"> the beta value </param>
/// <returns> the result </returns>
public virtual Pair <DoubleFunction1D, DoubleFunction1D>[] getPolynomialsAndFirstDerivative(int n, double alpha, double beta)
{
ArgChecker.isTrue(n >= 0);
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @SuppressWarnings("unchecked") com.opengamma.strata.collect.tuple.Pair<com.opengamma.strata.math.impl.function.DoubleFunction1D, com.opengamma.strata.math.impl.function.DoubleFunction1D>[] polynomials = new com.opengamma.strata.collect.tuple.Pair[n + 1];
Pair <DoubleFunction1D, DoubleFunction1D>[] polynomials = new Pair[n + 1];
DoubleFunction1D p, dp, p1, p2;
for (int i = 0; i <= n; i++)
{
if (i == 0)
{
polynomials[i] = Pair.of(One, Zero);
}
else if (i == 1)
{
double a1 = (alpha + beta + 2) / 2;
polynomials[i] = Pair.of((DoubleFunction1D) new RealPolynomialFunction1D(new double[] { (alpha - beta) / 2, a1 }), (DoubleFunction1D) new RealPolynomialFunction1D(new double[] { a1 }));
}
else
{
int j = i - 1;
p1 = polynomials[j].First;
p2 = polynomials[j - 1].First;
DoubleFunction1D temp1 = p1.multiply(getB(alpha, beta, j));
DoubleFunction1D temp2 = p1.multiply(X).multiply(getC(alpha, beta, j));
DoubleFunction1D temp3 = p2.multiply(getD(alpha, beta, j));
p = (temp1.add(temp2).add(temp3)).divide(getA(alpha, beta, j));
dp = p.derivative();
polynomials[i] = Pair.of(p, dp);
}
}
return(polynomials);
}
/// <summary>
/// Compute $A^T A$, where A is a matrix. </summary>
/// <param name="a"> The matrix </param>
/// <returns> The result of $A^T A$ </returns>
public virtual DoubleMatrix matrixTransposeMultiplyMatrix(DoubleMatrix a)
{
ArgChecker.notNull(a, "a");
int n = a.rowCount();
int m = a.columnCount();
//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);
for (int i = 0; i < m; i++)
{
double sum = 0d;
for (int k = 0; k < n; k++)
{
sum += a.get(k, i) * a.get(k, i);
}
data[i][i] = sum;
for (int j = i + 1; j < m; j++)
{
sum = 0d;
for (int k = 0; k < n; k++)
{
sum += a.get(k, i) * a.get(k, j);
}
data[i][j] = sum;
data[j][i] = sum;
}
}
return(DoubleMatrix.ofUnsafe(data));
}
/// <summary>
/// Adds two matrices. This operation can only be performed if the matrices are of the same type and dimensions. </summary>
/// <param name="m1"> The first matrix, not null </param>
/// <param name="m2"> The second matrix, not null </param>
/// <returns> The sum of the two matrices </returns>
/// <exception cref="IllegalArgumentException"> If the matrices are not of the same type, if the matrices are not the same shape. </exception>
public virtual Matrix add(Matrix m1, Matrix m2)
{
ArgChecker.notNull(m1, "m1");
ArgChecker.notNull(m2, "m2");
if (m1 is DoubleArray)
{
if (m2 is DoubleArray)
{
DoubleArray array1 = (DoubleArray)m1;
DoubleArray array2 = (DoubleArray)m2;
return(array1.plus(array2));
}
throw new System.ArgumentException("Tried to add a " + m1.GetType() + " and " + m2.GetType());
}
else if (m1 is DoubleMatrix)
{
if (m2 is DoubleMatrix)
{
DoubleMatrix matrix1 = (DoubleMatrix)m1;
DoubleMatrix matrix2 = (DoubleMatrix)m2;
return(matrix1.plus(matrix2));
}
throw new System.ArgumentException("Tried to add a " + m1.GetType() + " and " + m2.GetType());
}
throw new System.NotSupportedException();
}
/// <summary>
/// Creates an instance.
/// </summary>
/// <param name="a"> the value </param>
public IncompleteGammaFunction(double a)
{
ArgChecker.notNegativeOrZero(a, "a");
_maxIter = 100000;
_eps = 1e-12;
_a = a;
}
public override Pair <DoubleFunction1D, DoubleFunction1D>[] getPolynomialsAndFirstDerivative(int n)
{
ArgChecker.isTrue(n >= 0);
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @SuppressWarnings("unchecked") com.opengamma.strata.collect.tuple.Pair<com.opengamma.strata.math.impl.function.DoubleFunction1D, com.opengamma.strata.math.impl.function.DoubleFunction1D>[] polynomials = new com.opengamma.strata.collect.tuple.Pair[n + 1];
Pair <DoubleFunction1D, DoubleFunction1D>[] polynomials = new Pair[n + 1];
DoubleFunction1D p, dp;
for (int i = 0; i <= n; i++)
{
if (i == 0)
{
polynomials[i] = Pair.of(One, Zero);
}
else if (i == 1)
{
polynomials[i] = Pair.of(X, One);
}
else
{
p = (polynomials[i - 1].First.multiply(X).multiply(2 * i - 1).subtract(polynomials[i - 2].First.multiply(i - 1))).multiply(1.0 / i);
dp = p.derivative();
polynomials[i] = Pair.of(p, dp);
}
}
return(polynomials);
}
//-------------------------------------------------------------------------
public virtual System.Func <DoubleArray, DoubleMatrix[]> differentiate(System.Func <DoubleArray, DoubleArray> function, System.Func <DoubleArray, bool> domain)
{
ArgChecker.notNull(function, "function");
System.Func <DoubleArray, DoubleMatrix> jacFunc = vectorFieldDiff.differentiate(function, domain);
System.Func <DoubleArray, DoubleMatrix[]> hFunc = maxtrixFieldDiff.differentiate(jacFunc, domain);
return(new FuncAnonymousInnerClass2(this, hFunc));
}
/// <summary>
/// Creates an instance with a sampled (parameterised) curve.
/// </summary>
/// <param name="samplePoints"> the points where we sample the curve </param>
/// <param name="curve"> a parameterised curve </param>
public ParameterizedCurveVectorFunction(double[] samplePoints, ParameterizedCurve curve)
{
ArgChecker.notEmpty(samplePoints, "samplePoints");
ArgChecker.notNull(curve, "curve");
_samplePoints = Arrays.copyOf(samplePoints, samplePoints.Length);
_curve = curve;
}
/// <summary>
/// Creates an instance.
/// <para>
/// If the size of the domain is very small or very large, consider re-scaling first.
/// If this value is too small, the result will most likely be dominated by noise.
/// Use around 10<sup>-5</sup> times the domain size.
///
/// </para>
/// </summary>
/// <param name="differenceType"> the differencing type to be used in calculating the gradient function </param>
/// <param name="eps"> the step size used to approximate the derivative </param>
public VectorFieldFirstOrderDifferentiator(FiniteDifferenceType differenceType, double eps)
{
ArgChecker.notNull(differenceType, "differenceType");
this.differenceType = differenceType;
this.eps = eps;
this.twoEps = 2 * eps;
}
protected internal override QuantileResult expectedShortfall(double level, DoubleArray sample)
{
ArgChecker.isTrue(level > 0, "Quantile should be above 0.");
ArgChecker.isTrue(level < 1, "Quantile should be below 1.");
int sampleSize = sampleCorrection(sample.size());
double[] order = createIndexArray(sample.size());
double[] s = sample.toArray();
DoubleArrayMath.sortPairs(s, order);
double fractionalIndex = level * sampleSize;
int index = (int)checkIndex(this.index(fractionalIndex), sample.size(), true);
int[] indices = new int[index];
double[] weights = new double[index];
double interval = 1d / (double)sampleSize;
double losses = s[0] * interval *indexShift();
for (int i = 0; i < index - 1; i++)
{
losses += s[i] * interval;
indices[i] = (int)order[i];
weights[i] = interval;
}
losses += s[index - 1] * (fractionalIndex - index + 1 - indexShift()) * interval;
indices[index - 1] = (int)order[index - 1];
weights[0] += interval * indexShift();
weights[index - 1] = (fractionalIndex - index + 1 - indexShift()) * interval;
return(QuantileResult.of(losses / level, indices, DoubleArray.ofUnsafe(weights).dividedBy(level)));
}
/// <summary>
/// Creates an instance.
/// </summary>
/// <param name="x1"> the lower edge </param>
/// <param name="x2"> the upper edge, must be greater than x1 </param>
/// <param name="y"> the height </param>
public TopHatFunction(double x1, double x2, double y)
{
ArgChecker.isTrue(x1 < x2, "x1 must be less than x2");
_x1 = x1;
_x2 = x2;
_y = y;
}
/// <summary>
/// Gets the polynomials and derivative.
/// </summary>
/// <param name="n"> the n value </param>
/// <param name="alpha"> the alpha value </param>
/// <returns> the result </returns>
public virtual Pair <DoubleFunction1D, DoubleFunction1D>[] getPolynomialsAndFirstDerivative(int n, double alpha)
{
ArgChecker.isTrue(n >= 0);
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @SuppressWarnings("unchecked") com.opengamma.strata.collect.tuple.Pair<com.opengamma.strata.math.impl.function.DoubleFunction1D, com.opengamma.strata.math.impl.function.DoubleFunction1D>[] polynomials = new com.opengamma.strata.collect.tuple.Pair[n + 1];
Pair <DoubleFunction1D, DoubleFunction1D>[] polynomials = new Pair[n + 1];
DoubleFunction1D p, dp, p1, p2;
for (int i = 0; i <= n; i++)
{
if (i == 0)
{
polynomials[i] = Pair.of(One, Zero);
}
else if (i == 1)
{
polynomials[i] = Pair.of(F1, DF1);
}
else
{
p1 = polynomials[i - 1].First;
p2 = polynomials[i - 2].First;
p = (p1.multiply(2.0 * i + alpha - 1).subtract(p1.multiply(X)).subtract(p2.multiply((i - 1.0 + alpha))).divide(i));
dp = (p.multiply(i).subtract(p1.multiply(i + alpha))).divide(X);
polynomials[i] = Pair.of(p, dp);
}
}
return(polynomials);
}
public override DoubleMatrix apply(TridiagonalMatrix x)
{
ArgChecker.notNull(x, "x");
double[] a = x.DiagonalData;
double[] b = x.UpperSubDiagonalData;
double[] c = x.LowerSubDiagonalData;
int n = a.Length;
int i, j, k;
double[] theta = new double[n + 1];
double[] phi = new double[n];
theta[0] = 1.0;
theta[1] = a[0];
for (i = 2; i <= n; i++)
{
theta[i] = a[i - 1] * theta[i - 1] - b[i - 2] * c[i - 2] * theta[i - 2];
}
if (theta[n] == 0.0)
{
throw new MathException("Zero determinant. Cannot invert the matrix");
}
phi[n - 1] = 1.0;
phi[n - 2] = a[n - 1];
for (i = n - 3; i >= 0; i--)
{
phi[i] = a[i + 1] * phi[i + 1] - b[i + 1] * c[i + 1] * phi[i + 2];
}
double product;
//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[][] res = new double[n][n];
double[][] res = RectangularArrays.ReturnRectangularDoubleArray(n, n);
for (j = 0; j < n; j++)
{
for (i = 0; i <= j; i++)
{
product = 1.0;
for (k = i; k < j; k++)
{
product *= b[k];
}
res[i][j] = ((i + j) % 2 == 0 ? 1 : -1) * product * theta[i] * phi[j] / theta[n];
}
for (i = j + 1; i < n; i++)
{
product = 1.0;
for (k = j; k < i; k++)
{
product *= c[k];
}
res[i][j] = ((i + j) % 2 == 0 ? 1 : -1) * product * theta[j] * phi[i] / theta[n];
}
}
return(DoubleMatrix.copyOf(res));
}
/// <summary>
/// Gamma is in the form gamma^i_{j,k} =\partial^2y_j/\partial x_i \partial x_k, where i is the
/// index of the matrix in the stack (3rd index of the tensor), and j,k are the individual
/// matrix indices. We would like it in the form H^i_{j,k} =\partial^2y_i/\partial x_j \partial x_k,
/// so that each matrix is a Hessian (for the dependent variable y_i), hence the reshaping below.
/// </summary>
/// <param name="gamma"> the rank 3 tensor </param>
/// <returns> the reshaped rank 3 tensor </returns>
private DoubleMatrix[] reshapeTensor(DoubleMatrix[] gamma)
{
int m = gamma.Length;
int n = gamma[0].rowCount();
ArgChecker.isTrue(gamma[0].columnCount() == m, "tenor wrong size. Seond index is {}, should be {}", gamma[0].columnCount(), m);
DoubleMatrix[] res = new DoubleMatrix[n];
for (int i = 0; i < n; i++)
{
//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[][] temp = new double[m][m];
double[][] temp = RectangularArrays.ReturnRectangularDoubleArray(m, m);
for (int j = 0; j < m; j++)
{
DoubleMatrix gammaJ = gamma[j];
for (int k = j; k < m; k++)
{
temp[j][k] = gammaJ.get(i, k);
}
}
for (int j = 0; j < m; j++)
{
for (int k = 0; k < j; k++)
{
temp[j][k] = temp[k][j];
}
}
res[i] = DoubleMatrix.copyOf(temp);
}
return(res);
}
public static int[] fromTensorIndex(int index, int[] dimensions)
{
ArgChecker.notNull(dimensions, "dimensions");
int dim = dimensions.Length;
int[] res = new int[dim];
int product = 1;
int[] products = new int[dim - 1];
for (int i = 0; i < dim - 1; i++)
{
product *= dimensions[i];
products[i] = product;
}
int a = index;
for (int i = dim - 1; i > 0; i--)
{
res[i] = a / products[i - 1];
a -= res[i] * products[i - 1];
}
res[0] = a;
return(res);
}
public virtual double?[] getRoots(RealPolynomialFunction1D function)
{
ArgChecker.notNull(function, "function");
double[] coeffs = function.Coefficients;
int l = coeffs.Length - 1;
//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[][] hessianDeref = new double[l][l];
double[][] hessianDeref = RectangularArrays.ReturnRectangularDoubleArray(l, l);
for (int i = 0; i < l; i++)
{
hessianDeref[0][i] = -coeffs[l - i - 1] / coeffs[l];
for (int j = 1; j < l; j++)
{
hessianDeref[j][i] = 0;
if (i != l - 1)
{
hessianDeref[i + 1][i] = 1;
}
}
}
RealMatrix hessian = new Array2DRowRealMatrix(hessianDeref);
double[] d = (new EigenDecomposition(hessian)).RealEigenvalues;
double?[] result = new double?[d.Length];
for (int i = 0; i < d.Length; i++)
{
result[i] = d[i];
}
return(result);
}
/// <summary>
/// Finds the node sensitivity.
/// </summary>
/// <param name="pp"> the <seealso cref="PiecewisePolynomialResultsWithSensitivity"/> </param>
/// <param name="xKey"> the key </param>
/// <returns> Node sensitivity value at x=xKey </returns>
public virtual DoubleArray nodeSensitivity(PiecewisePolynomialResultsWithSensitivity pp, double xKey)
{
ArgChecker.notNull(pp, "null pp");
ArgChecker.isFalse(double.IsNaN(xKey), "xKey containing NaN");
ArgChecker.isFalse(double.IsInfinity(xKey), "xKey containing Infinity");
if (pp.Dimensions > 1)
{
throw new System.NotSupportedException();
}
DoubleArray knots = pp.Knots;
int nKnots = knots.size();
int interval = FunctionUtils.getLowerBoundIndex(knots, xKey);
if (interval == nKnots - 1)
{
interval--; // there is 1 less interval that knots
}
double s = xKey - knots.get(interval);
DoubleMatrix a = pp.getCoefficientSensitivity(interval);
int nCoefs = a.rowCount();
DoubleArray res = a.row(0);
for (int i = 1; i < nCoefs; i++)
{
res = (DoubleArray)MA.scale(res, s);
res = (DoubleArray)MA.add(res, a.row(i));
}
return(res);
}
//-------------------------------------------------------------------------
/// <summary>
/// Calculates the price sensitivity of the bond future option product based on curves.
/// <para>
/// The price sensitivity of the product is the sensitivity of the price to the underlying curves.
/// The volatility is unchanged for a fixed strike in the sensitivity computation, hence the "StickyStrike" name.
/// </para>
/// <para>
/// This calculates the underlying future price using the future pricer.
///
/// </para>
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="discountingProvider"> the discounting provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <returns> the price curve sensitivity of the product </returns>
public PointSensitivities priceSensitivityRatesStickyStrike(ResolvedBondFutureOption futureOption, LegalEntityDiscountingProvider discountingProvider, BlackBondFutureVolatilities volatilities)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double futurePrice = this.futurePrice(futureOption, discountingProvider);
return(priceSensitivityRatesStickyStrike(futureOption, discountingProvider, volatilities, futurePrice));
}
/// <summary>
/// Solves the system Ax = y for the unknown vector x, where A is a tridiagonal matrix and y is a vector.
/// This takes order n operations where n is the size of the system
/// (number of linear equations), as opposed to order n^3 for the general problem. </summary>
/// <param name="aM"> tridiagonal matrix </param>
/// <param name="b"> known vector (must be same length as rows/columns of matrix) </param>
/// <returns> vector (as an array of doubles) with same length as y </returns>
public static double[] solvTriDag(TridiagonalMatrix aM, double[] b)
{
ArgChecker.notNull(aM, "null matrix");
ArgChecker.notNull(b, "null vector");
double[] d = aM.Diagonal; //b is modified, so get copy of diagonal
int n = d.Length;
ArgChecker.isTrue(n == b.Length, "vector y wrong length for matrix");
double[] y = Arrays.copyOf(b, n);
double[] l = aM.LowerSubDiagonalData;
double[] u = aM.UpperSubDiagonalData;
double[] x = new double[n];
for (int i = 1; i < n; i++)
{
double m = l[i - 1] / d[i - 1];
d[i] = d[i] - m * u[i - 1];
y[i] = y[i] - m * y[i - 1];
}
x[n - 1] = y[n - 1] / d[n - 1];
for (int i = n - 2; i >= 0; i--)
{
x[i] = (y[i] - u[i] * x[i + 1]) / d[i];
}
return(x);
}
public virtual double pvbp(RatePaymentPeriod paymentPeriod, RatesProvider provider)
{
ArgChecker.isTrue(!paymentPeriod.FxReset.Present, "FX reset is not supported");
int accPeriodCount = paymentPeriod.AccrualPeriods.size();
ArgChecker.isTrue(accPeriodCount == 1 || paymentPeriod.CompoundingMethod.Equals(CompoundingMethod.FLAT), "Only one accrued period or Flat compounding supported");
// no compounding
if (accPeriodCount == 1)
{
RateAccrualPeriod accrualPeriod = paymentPeriod.AccrualPeriods.get(0);
double df = provider.discountFactor(paymentPeriod.Currency, paymentPeriod.PaymentDate);
return(df * accrualPeriod.YearFraction * paymentPeriod.Notional);
}
else
{
// Flat compounding
switch (paymentPeriod.CompoundingMethod)
{
case FLAT:
return(pvbpCompoundedFlat(paymentPeriod, provider));
default:
throw new System.NotSupportedException("PVBP not implemented yet for non FLAT compounding");
}
}
}
/// <summary>
/// Uses the parameters to create a function.
/// </summary>
/// <param name="x"> the value at which the function is to be evaluated, not null </param>
/// <returns> a function that is always evaluated at <i>x</i> for different values of the parameters </returns>
public virtual System.Func <T, U> asFunctionOfParameters(S x)
{
ArgChecker.notNull(x, "x");
return((T @params) =>
{
return ParameterizedFunction.this.evaluate(x, @params);
});
}
/// <summary>
/// Constructor. </summary>
/// <param name="ch"> The result of the Cholesky decomposition. </param>
public CholeskyDecompositionCommonsResult(CholeskyDecomposition ch)
{
ArgChecker.notNull(ch, "Cholesky decomposition");
_determinant = ch.Determinant;
_l = CommonsMathWrapper.unwrap(ch.L);
_lt = CommonsMathWrapper.unwrap(ch.LT);
_solver = ch.Solver;
}
protected internal virtual void checkInputs(System.Func <double, double> f, double xLower, double xUpper)
{
ArgChecker.notNull(f, "function");
if (DoubleMath.fuzzyEquals(xLower, xUpper, ZERO))
{
throw new System.ArgumentException("Lower and upper values were not distinct");
}
}
/// <summary>
/// {@inheritDoc}
/// </summary>
public virtual double?integrate(System.Func <double, double> function, double?lower, double?upper)
{
ArgChecker.notNull(function, "function");
ArgChecker.notNull(lower, "lower");
ArgChecker.notNull(upper, "upper");
System.Func <double, double> integral = getIntegralFunction(function, lower, upper);
return(integrateFromPolyFunc(integral));
}
/// <summary>
/// Creates an instance.
/// </summary>
/// <param name="n"> The number of sample points to be used in the integration, not negative or zero </param>
/// <param name="generator"> The generator of weights and abscissas </param>
public GaussianQuadratureIntegrator1D(int n, QuadratureWeightAndAbscissaFunction generator)
{
ArgChecker.isTrue(n > 0, "number of intervals must be > 0");
ArgChecker.notNull(generator, "generating function");
this.size = n;
this.generator = generator;
this.quadrature = generator.generate(size);
}
/// <param name="abscissas"> An array containing the abscissas, not null </param>
/// <param name="weights"> An array containing the weights, not null, must be the same length as the abscissa array </param>
public GaussianQuadratureData(double[] abscissas, double[] weights)
{
ArgChecker.notNull(abscissas, "abscissas");
ArgChecker.notNull(weights, "weights");
ArgChecker.isTrue(abscissas.Length == weights.Length, "Abscissa and weight arrays must be the same length");
_weights = weights;
_abscissas = abscissas;
}
public virtual LUDecompositionResult apply(DoubleMatrix x)
{
ArgChecker.notNull(x, "x");
RealMatrix temp = CommonsMathWrapper.wrap(x);
LUDecomposition lu = new LUDecomposition(temp);
return(new LUDecompositionCommonsResult(lu));
}
/// <summary>
/// Uses the parameters to create a function.
/// </summary>
/// <param name="params"> the parameters for which the function is to be evaluated, not null </param>
/// <returns> a function that can be evaluated at different <i>x</i> with the input parameters </returns>
public virtual System.Func <S, U> asFunctionOfArguments(T @params)
{
ArgChecker.notNull(@params, "params");
return((S x) =>
{
return ParameterizedFunction.this.evaluate(x, @params);
});
}
//-------------------------------------------------------------------------
/// <summary>
/// Calculates the price sensitivity of the Ibor future option product based on curves.
/// <para>
/// The price sensitivity of the product is the sensitivity of the price to the underlying curves.
/// The volatility is unchanged for a fixed strike in the sensitivity computation, hence the "StickyStrike" name.
/// </para>
/// <para>
/// This calculates the underlying future price using the future pricer.
///
/// </para>
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="ratesProvider"> the rates provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <returns> the price curve sensitivity of the product </returns>
public virtual PointSensitivities priceSensitivityRatesStickyStrike(ResolvedIborFutureOption futureOption, RatesProvider ratesProvider, NormalIborFutureOptionVolatilities volatilities)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double futurePrice = this.futurePrice(futureOption, ratesProvider);
return(priceSensitivityRatesStickyStrike(futureOption, ratesProvider, volatilities, futurePrice));
}
public override double?apply(double[] x)
{
ArgChecker.notNull(x, "x");
int n = x.Length;
ArgChecker.isTrue(n >= 2, "Need at least two points to calculate the population variance");
return(_variance.apply(x) * (n - 1) / n);
}
