//-------------------------------------------------------------------------
/// <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);
}
/// <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>
/// 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 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);
}
/// <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);
}
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)));
}
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>
/// 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);
}
//-------------------------------------------------------------------------
/// <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));
}
/// <summary>
/// Returns the quotient of two matrices $C = \frac{A}{B} = AB^{-1}$, where
/// $B^{-1}$ is the pseudo-inverse of $B$ i.e. $BB^{-1} = \mathbb{1}$. </summary>
/// <param name="m1"> The numerator matrix, not null. This matrix must be a <seealso cref="DoubleMatrix"/>. </param>
/// <param name="m2"> The denominator, not null. This matrix must be a <seealso cref="DoubleMatrix"/>. </param>
/// <returns> The result </returns>
public virtual Matrix divide(Matrix m1, Matrix m2)
{
ArgChecker.notNull(m1, "m1");
ArgChecker.notNull(m2, "m2");
ArgChecker.isTrue(m1 is DoubleMatrix, "Can only divide a 2D matrix");
ArgChecker.isTrue(m2 is DoubleMatrix, "Can only perform division with a 2D matrix");
return(multiply(m1, getInverse(m2)));
}
/// <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;
}
/// <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);
}
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);
}
/// <summary>
/// Tests that the inputs to the root-finder are not null, and that a root is bracketed by the bounding values.
/// </summary>
/// <param name="function"> The function, not null </param>
/// <param name="x1"> The first bound, not null </param>
/// <param name="x2"> The second bound, not null, must be greater than x1 </param>
/// <exception cref="IllegalArgumentException"> if x1 and x2 do not bracket a root </exception>
protected internal virtual void checkInputs(DoubleFunction1D function, double?x1, double?x2)
{
ArgChecker.notNull(function, "function");
ArgChecker.notNull(x1, "x1");
ArgChecker.notNull(x2, "x2");
ArgChecker.isTrue(x1 <= x2, "x1 must be less or equal to x2");
ArgChecker.isTrue(function.applyAsDouble(x1) * function.applyAsDouble(x2) <= 0, "x1 and x2 do not bracket a root");
}
//-------------------------------------------------------------------------
// gets the settlement price
private double settlementPrice(ResolvedDsfTrade trade, RatesProvider ratesProvider)
{
StandardId standardId = trade.Product.SecurityId.StandardId;
QuoteId id = QuoteId.of(standardId, FieldName.SETTLEMENT_PRICE);
double price = ratesProvider.data(id);
ArgChecker.isTrue(price < 10, "Price must be in decimal form, such as 1.007 for a 0.7% present value, but was: {}", price);
return(price);
}
protected internal virtual double[] writeArrayAsVector(double[][] x)
{
ArgChecker.isTrue(x[0].Length == 1, "Trying to convert matrix to vector");
double[] result = new double[x.Length];
for (int i = 0; i < x.Length; i++)
{
result[i] = x[i][0];
}
return(result);
}
/// <summary>
/// Calculates the delta of the Ibor future option product
/// based on the price of the underlying future.
/// <para>
/// The delta of the product is the sensitivity of the option price to the future price.
/// The volatility is unchanged for a fixed strike in the sensitivity computation, hence the "StickyStrike" name.
///
/// </para>
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="ratesProvider"> the rates provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <param name="futurePrice"> the price of the underlying future, in decimal form </param>
/// <returns> the price curve sensitivity of the product </returns>
public virtual double deltaStickyStrike(ResolvedIborFutureOption futureOption, RatesProvider ratesProvider, NormalIborFutureOptionVolatilities volatilities, double futurePrice)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double timeToExpiry = volatilities.relativeTime(futureOption.Expiry);
double strike = futureOption.StrikePrice;
ResolvedIborFuture future = futureOption.UnderlyingFuture;
double volatility = volatilities.volatility(timeToExpiry, future.LastTradeDate, strike, futurePrice);
return(NormalFormulaRepository.delta(futurePrice, strike, timeToExpiry, volatility, futureOption.PutCall));
}
//-------------------------------------------------------------------------
/// <summary>
/// Evaluates the function.
/// </summary>
/// <param name="x"> The argument of the function, not null. Must have $x_1 < x < x_2$ </param>
/// <returns> The value of the function </returns>
public override double?apply(double?x)
{
ArgChecker.notNull(x, "x");
ArgChecker.isTrue(x.Value != _x1, "Function is undefined for x = x1");
ArgChecker.isTrue(x.Value != _x2, "Function is undefined for x = x2");
if (x.Value > _x1 && x.Value < _x2)
{
return(_y);
}
return(0.0);
}
/// <summary>
/// Calculates the theta of the bond future option product based on the price of the underlying future.
/// <para>
/// The theta of the product is minus of the option price sensitivity to the time to expiry.
/// The volatility is unchanged for a fixed strike in the sensitivity computation, hence the "StickyStrike" name.
///
/// </para>
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="discountingProvider"> the discounting provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <param name="futurePrice"> the price of the underlying future </param>
/// <returns> the price curve sensitivity of the product </returns>
public double theta(ResolvedBondFutureOption futureOption, LegalEntityDiscountingProvider discountingProvider, BlackBondFutureVolatilities volatilities, double futurePrice)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double strike = futureOption.StrikePrice;
ResolvedBondFuture future = futureOption.UnderlyingFuture;
double volatility = volatilities.volatility(futureOption.Expiry, future.LastTradeDate, strike, futurePrice);
double timeToExpiry = volatilities.relativeTime(futureOption.Expiry);
double theta = BlackFormulaRepository.driftlessTheta(futurePrice, strike, timeToExpiry, volatility);
return(theta);
}
/// <summary>
/// Creates an instance.
/// </summary>
/// <param name="a"> a, $a > 0$ </param>
/// <param name="b"> b, $b > 0$ </param>
/// <param name="eps"> approximation accuracy, $\epsilon \geq 0$ </param>
/// <param name="maxIter"> maximum number of iterations, $\iter \geq 1$ </param>
public IncompleteBetaFunction(double a, double b, double eps, int maxIter)
{
ArgChecker.isTrue(a > 0, "a must be > 0");
ArgChecker.isTrue(b > 0, "b must be > 0");
ArgChecker.isTrue(eps >= 0, "eps must not be negative");
ArgChecker.isTrue(maxIter >= 1, "maximum number of iterations must be greater than zero");
_a = a;
_b = b;
_eps = eps;
_maxIter = maxIter;
}
/// <summary>
/// Calculates the price sensitivity to the Black volatility used for the pricing of the bond future option
/// based on the price of the underlying future.
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="discountingProvider"> the discounting provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <param name="futurePrice"> the underlying future price </param>
/// <returns> the sensitivity </returns>
public BondFutureOptionSensitivity priceSensitivityModelParamsVolatility(ResolvedBondFutureOption futureOption, LegalEntityDiscountingProvider discountingProvider, BlackBondFutureVolatilities volatilities, double futurePrice)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double strike = futureOption.StrikePrice;
ResolvedBondFuture future = futureOption.UnderlyingFuture;
double volatility = volatilities.volatility(futureOption.Expiry, future.LastTradeDate, strike, futurePrice);
double timeToExpiry = volatilities.relativeTime(futureOption.Expiry);
double vega = BlackFormulaRepository.vega(futurePrice, strike, timeToExpiry, volatility);
return(BondFutureOptionSensitivity.of(volatilities.Name, timeToExpiry, future.LastTradeDate, strike, futurePrice, future.Currency, vega));
}
//-------------------------------------------------------------------------
/// <summary>
/// Evaluates the function.
/// </summary>
/// <param name="x"> x </param>
/// <returns> the value of the function </returns>
/// <exception cref="IllegalArgumentException"> if $x < 0$ or $x > 1$ </exception>
public override double?apply(double?x)
{
ArgChecker.isTrue(x >= 0 && x <= 1, "x must be in the range 0 to 1");
try
{
return(Beta.regularizedBeta(x, _a, _b, _eps, _maxIter));
}
catch (MaxCountExceededException e)
{
throw new MathException(e);
}
}
/// <param name="a"> An array containing the diagonal values of the matrix, not null </param>
/// <param name="b"> An array containing the upper sub-diagonal values of the matrix, not null.
/// Its length must be one less than the length of the diagonal array </param>
/// <param name="c"> An array containing the lower sub-diagonal values of the matrix, not null.
/// Its length must be one less than the length of the diagonal array </param>
public TridiagonalMatrix(double[] a, double[] b, double[] c)
{
ArgChecker.notNull(a, "a");
ArgChecker.notNull(b, "b");
ArgChecker.notNull(c, "c");
int n = a.Length;
ArgChecker.isTrue(b.Length == n - 1, "Length of subdiagonal b is incorrect");
ArgChecker.isTrue(c.Length == n - 1, "Length of subdiagonal c is incorrect");
_a = a;
_b = b;
_c = c;
}
/// <summary>
/// Calculates the price sensitivity to the normal volatility used for the pricing of the Ibor future option
/// based on the price of the underlying future.
/// <para>
/// This sensitivity is also called the <i>price normal vega</i>.
///
/// </para>
/// </summary>
/// <param name="futureOption"> the option product </param>
/// <param name="ratesProvider"> the rates provider </param>
/// <param name="volatilities"> the volatilities </param>
/// <param name="futurePrice"> the underlying future price, in decimal form </param>
/// <returns> the sensitivity </returns>
public virtual IborFutureOptionSensitivity priceSensitivityModelParamsVolatility(ResolvedIborFutureOption futureOption, RatesProvider ratesProvider, NormalIborFutureOptionVolatilities volatilities, double futurePrice)
{
ArgChecker.isTrue(futureOption.PremiumStyle.Equals(FutureOptionPremiumStyle.DAILY_MARGIN), "Premium style should be DAILY_MARGIN");
double timeToExpiry = volatilities.relativeTime(futureOption.Expiry);
double strike = futureOption.StrikePrice;
ResolvedIborFuture future = futureOption.UnderlyingFuture;
double volatility = volatilities.volatility(timeToExpiry, future.LastTradeDate, strike, futurePrice);
double vega = NormalFormulaRepository.vega(futurePrice, strike, timeToExpiry, volatility, futureOption.PutCall);
return(IborFutureOptionSensitivity.of(volatilities.Name, timeToExpiry, future.LastTradeDate, strike, futurePrice, future.Currency, vega));
}
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @SuppressWarnings("synthetic-access") @Override public com.opengamma.strata.collect.array.DoubleMatrix[] apply(com.opengamma.strata.collect.array.DoubleArray x)
public override DoubleMatrix[] apply(DoubleArray x)
{
ArgChecker.notNull(x, "x");
ArgChecker.isTrue(domain(x), "point {} is not in the function domain", x.ToString());
int n = x.size();
DoubleMatrix[] y = new DoubleMatrix[3];
DoubleMatrix[] res = new DoubleMatrix[n];
double[] w;
for (int i = 0; i < n; i++)
{
double xi = x.get(i);
DoubleArray xPlusOneEps = x.with(i, xi + outerInstance.eps);
DoubleArray xMinusOneEps = x.with(i, xi - outerInstance.eps);
if (!domain(xPlusOneEps))
{
DoubleArray xMinusTwoEps = x.with(i, xi - outerInstance.twoEps);
if (!domain(xMinusTwoEps))
{
throw new MathException("cannot get derivative at point " + x.ToString() + " in direction " + i);
}
y[0] = function(xMinusTwoEps);
y[2] = function(x);
y[1] = function(xMinusOneEps);
w = wBack;
}
else
{
if (!domain(xMinusOneEps))
{
y[1] = function(xPlusOneEps);
y[0] = function(x);
y[2] = function(x.with(i, xi + outerInstance.twoEps));
w = wFwd;
}
else
{
y[2] = function(xPlusOneEps);
y[0] = function(xMinusOneEps);
w = wCent;
}
}
res[i] = (DoubleMatrix)MA.add(MA.scale(y[0], w[0]), MA.scale(y[2], w[2]));
if (w[1] != 0)
{
res[i] = (DoubleMatrix)MA.add(res[i], MA.scale(y[1], w[1]));
}
}
return(res);
}
/// <summary>
/// Creates an instance.
/// </summary>
/// <param name="xValues"> the x-values of the curve, must be sorted from low to high </param>
/// <param name="yValues"> the y-values of the curve </param>
protected internal AbstractBoundCurveInterpolator(DoubleArray xValues, DoubleArray yValues)
{
ArgChecker.notNull(xValues, "xValues");
ArgChecker.notNull(yValues, "yValues");
int size = xValues.size();
ArgChecker.isTrue(size == yValues.size(), "Curve node arrays must have same size");
ArgChecker.isTrue(size > 1, "Curve node arrays must have at least two nodes");
this.extrapolatorLeft = ExceptionCurveExtrapolator.INSTANCE;
this.extrapolatorRight = ExceptionCurveExtrapolator.INSTANCE;
this.firstXValue = xValues.get(0);
this.lastXValue = xValues.get(size - 1);
this.lastYValue = yValues.get(size - 1);
}
public LeastSquareResults(double chiSq, DoubleArray parameters, DoubleMatrix covariance, DoubleMatrix inverseJacobian)
{
ArgChecker.isTrue(chiSq >= 0, "chi square < 0");
ArgChecker.notNull(parameters, "parameters");
ArgChecker.notNull(covariance, "covariance");
int n = parameters.size();
ArgChecker.isTrue(covariance.columnCount() == n, "covariance matrix not square");
ArgChecker.isTrue(covariance.rowCount() == n, "covariance matrix wrong size");
//TODO test size of inverse Jacobian
_chiSq = chiSq;
_parameters = parameters;
_covariance = covariance;
_inverseJacobian = inverseJacobian;
}
protected internal override QuantileResult quantile(double level, DoubleArray sample, bool isExtrapolated)
{
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);
int index = (int)checkIndex(this.index(level * sampleSize), sample.size(), isExtrapolated);
int[] ind = new int[1];
ind[0] = (int)order[index - 1];
return(QuantileResult.of(s[index - 1], ind, DoubleArray.of(1)));
}
