public InvCumulativeSobolGaussianRsg(SobolRsg uniformSequenceGenerator, InverseCumulativeNormal inverseCumulative) : this(NQuantLibcPINVOKE.new_InvCumulativeSobolGaussianRsg__SWIG_1(SobolRsg.getCPtr(uniformSequenceGenerator), InverseCumulativeNormal.getCPtr(inverseCumulative)), true) { if (NQuantLibcPINVOKE.SWIGPendingException.Pending) { throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve(); } }
public void RiskStatisticsTest() { // ("Testing risk measures..."); IncrementalGaussianStatistics igs = new IncrementalGaussianStatistics(); RiskStatistics s = new RiskStatistics(); double[] averages = { -100.0, -1.0, 0.0, 1.0, 100.0 }; double[] sigmas = { 0.1, 1.0, 100.0 }; int i, j, k, N; N = (int)Math.Pow(2, 16) - 1; double dataMin, dataMax; List <double> data = new InitializedList <double>(N), weights = new InitializedList <double>(N); for (i = 0; i < averages.Length; i++) { for (j = 0; j < sigmas.Length; j++) { NormalDistribution normal = new NormalDistribution(averages[i], sigmas[j]); CumulativeNormalDistribution cumulative = new CumulativeNormalDistribution(averages[i], sigmas[j]); InverseCumulativeNormal inverseCum = new InverseCumulativeNormal(averages[i], sigmas[j]); SobolRsg rng = new SobolRsg(1); dataMin = double.MaxValue; dataMax = double.MinValue; for (k = 0; k < N; k++) { data[k] = inverseCum.value(rng.nextSequence().value[0]); dataMin = Math.Min(dataMin, data[k]); dataMax = Math.Max(dataMax, data[k]); weights[k] = 1.0; } igs.addSequence(data, weights); s.addSequence(data, weights); // checks double calculated, expected; double tolerance; if (igs.samples() != N) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong number of samples\n" + " calculated: " + igs.samples() + "\n" + " expected: " + N); } if (s.samples() != N) { QAssert.Fail("RiskStatistics: wrong number of samples\n" + " calculated: " + s.samples() + "\n" + " expected: " + N); } // weightSum() tolerance = 1e-10; expected = weights.Sum(); calculated = igs.weightSum(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong sum of weights\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.weightSum(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong sum of weights\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // min tolerance = 1e-12; expected = dataMin; calculated = igs.min(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong minimum value\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.min(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: " + "wrong minimum value\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // max expected = dataMax; calculated = igs.max(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong maximum value\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.max(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: " + "wrong maximum value\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // mean expected = averages[i]; tolerance = (expected == 0.0 ? 1.0e-13 : Math.Abs(expected) * 1.0e-13); calculated = igs.mean(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong mean value" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.mean(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong mean value" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // variance expected = sigmas[j] * sigmas[j]; tolerance = expected * 1.0e-1; calculated = igs.variance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong variance" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.variance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong variance" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // standardDeviation expected = sigmas[j]; tolerance = expected * 1.0e-1; calculated = igs.standardDeviation(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong standard deviation" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.standardDeviation(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong standard deviation" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // missing errorEstimate() test // skewness expected = 0.0; tolerance = 1.0e-4; calculated = igs.skewness(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong skewness" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.skewness(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong skewness" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // kurtosis expected = 0.0; tolerance = 1.0e-1; calculated = igs.kurtosis(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong kurtosis" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.kurtosis(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong kurtosis" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // percentile expected = averages[i]; tolerance = (expected == 0.0 ? 1.0e-3 : Math.Abs(expected * 1.0e-3)); calculated = igs.gaussianPercentile(0.5); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian percentile" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianPercentile(0.5); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian percentile" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.percentile(0.5); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong percentile" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // potential upside double upper_tail = averages[i] + 2.0 * sigmas[j], lower_tail = averages[i] - 2.0 * sigmas[j]; double twoSigma = cumulative.value(upper_tail); expected = Math.Max(upper_tail, 0.0); tolerance = (expected == 0.0 ? 1.0e-3 : Math.Abs(expected * 1.0e-3)); calculated = igs.gaussianPotentialUpside(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian potential upside" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianPotentialUpside(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian potential upside" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.potentialUpside(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong potential upside" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // just to check that GaussianStatistics<StatsHolder> does work StatsHolder h = new StatsHolder(s.mean(), s.standardDeviation()); GenericGaussianStatistics <StatsHolder> test = new GenericGaussianStatistics <StatsHolder>(h); expected = s.gaussianPotentialUpside(twoSigma); calculated = test.gaussianPotentialUpside(twoSigma); if (calculated != expected) { QAssert.Fail("GenericGaussianStatistics<StatsHolder> fails" + "\n calculated: " + calculated + "\n expected: " + expected); } // value-at-risk expected = -Math.Min(lower_tail, 0.0); tolerance = (expected == 0.0 ? 1.0e-3 : Math.Abs(expected * 1.0e-3)); calculated = igs.gaussianValueAtRisk(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian value-at-risk" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianValueAtRisk(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian value-at-risk" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.valueAtRisk(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong value-at-risk" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } if (averages[i] > 0.0 && sigmas[j] < averages[i]) { // no data will miss the targets: // skip the rest of this iteration igs.reset(); s.reset(); continue; } // expected shortfall expected = -Math.Min(averages[i] - sigmas[j] * sigmas[j] * normal.value(lower_tail) / (1.0 - twoSigma), 0.0); tolerance = (expected == 0.0 ? 1.0e-4 : Math.Abs(expected) * 1.0e-2); calculated = igs.gaussianExpectedShortfall(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian expected shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianExpectedShortfall(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian expected shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.expectedShortfall(twoSigma); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong expected shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // shortfall expected = 0.5; tolerance = (expected == 0.0 ? 1.0e-3 : Math.Abs(expected * 1.0e-3)); calculated = igs.gaussianShortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianShortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.shortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // average shortfall expected = sigmas[j] / Math.Sqrt(2.0 * Const.M_PI) * 2.0; tolerance = expected * 1.0e-3; calculated = igs.gaussianAverageShortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian average shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianAverageShortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian average shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.averageShortfall(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong average shortfall" + " for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // regret expected = sigmas[j] * sigmas[j]; tolerance = expected * 1.0e-1; calculated = igs.gaussianRegret(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian regret(" + averages[i] + ") " + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianRegret(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: " + "wrong Gaussian regret(" + averages[i] + ") " + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.regret(averages[i]); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: " + "wrong regret(" + averages[i] + ") " + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // downsideVariance expected = s.downsideVariance(); tolerance = (expected == 0.0 ? 1.0e-3 : Math.Abs(expected * 1.0e-3)); calculated = igs.downsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = igs.gaussianDownsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } // downsideVariance if (averages[i] == 0.0) { expected = sigmas[j] * sigmas[j]; tolerance = expected * 1.0e-3; calculated = igs.downsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = igs.gaussianDownsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("IncrementalGaussianStatistics: " + "wrong Gaussian downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.downsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } calculated = s.gaussianDownsideVariance(); if (Math.Abs(calculated - expected) > tolerance) { QAssert.Fail("RiskStatistics: wrong Gaussian downside variance" + "for N(" + averages[i] + ", " + sigmas[j] + ")\n" + " calculated: " + calculated + "\n" + " expected: " + expected + "\n" + " tolerance: " + tolerance); } } igs.reset(); s.reset(); } } }
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(InverseCumulativeNormal obj) { return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr); }
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(InverseCumulativeNormal obj) { return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr; }
protected double mcReferenceValue(CmsCoupon cpn1, CmsCoupon cpn2, double cap, double floor, Handle <SwaptionVolatilityStructure> vol, double correlation) { List <double> acc = new List <double>(); int samples = 1000000; Matrix Cov = new Matrix(2, 2); Cov[0, 0] = vol.currentLink().blackVariance(cpn1.fixingDate(), cpn1.index().tenor(), cpn1.indexFixing()); Cov[1, 1] = vol.currentLink().blackVariance(cpn2.fixingDate(), cpn2.index().tenor(), cpn2.indexFixing()); Cov[0, 1] = Cov[1, 0] = Math.Sqrt(Cov[0, 0] * Cov[1, 1]) * correlation; Matrix C = MatrixUtilitites.pseudoSqrt(Cov, MatrixUtilitites.SalvagingAlgorithm.None); List <double> atmRate = new InitializedList <double>(2), adjRate = new InitializedList <double>(2), volShift = new InitializedList <double>(2); Vector avg = new Vector(2); atmRate[0] = cpn1.indexFixing(); atmRate[1] = cpn2.indexFixing(); adjRate[0] = cpn1.adjustedFixing; adjRate[1] = cpn2.adjustedFixing; if (vol.currentLink().volatilityType() == VolatilityType.ShiftedLognormal) { volShift[0] = vol.currentLink().shift(cpn1.fixingDate(), cpn1.index().tenor()); volShift[1] = vol.currentLink().shift(cpn2.fixingDate(), cpn2.index().tenor()); avg[0] = Math.Log((adjRate[0] + volShift[0]) / (atmRate[0] + volShift[0])) - 0.5 * Cov[0, 0]; avg[1] = Math.Log((adjRate[1] + volShift[1]) / (atmRate[1] + volShift[1])) - 0.5 * Cov[1, 1]; } else { avg[0] = adjRate[0]; avg[1] = adjRate[1]; } InverseCumulativeNormal icn = new InverseCumulativeNormal(); SobolRsg sb_ = new SobolRsg(2, 42); Vector w = new Vector(2), z = new Vector(2); for (int i = 0; i < samples; ++i) { List <double> seq = sb_.nextSequence().value; w[0] = icn.value(seq[0]); w[1] = icn.value(seq[1]); z = C * w + avg; for (int j = 0; j < 2; ++j) { if (vol.currentLink().volatilityType() == VolatilityType.ShiftedLognormal) { z[j] = (atmRate[j] + volShift[j]) * Math.Exp(z[j]) - volShift[j]; } } acc.Add(Math.Min(Math.Max(z[0] - z[1], floor), cap)); } return(acc.Average()); } // mcReferenceValue