/// <summary>
/// Initialize a new MonteCarloModel optionally using control variate techniques.
/// </summary>
/// <remarks>
/// In order to use the control variate technique, the user should provide the
/// additional control option, namely the <paramref name="cvPathPricer"/> and
/// the <paramref name="cvOptionValue"/>.
/// </remarks>
/// <param name="pathGenerator">
/// The <see cref="PathGenerator"/> generating single or multiple random walks.
/// </param>
/// <param name="pathPricer">
/// The <see cref="PathPricer"/> calculating the option price on each sample.
/// </param>
/// <param name="sampleAccumulator">
/// A sample accumulator to record statistics.
/// </param>
/// <param name="cvPathPricer">
/// The control variate <see cref="PathPricer"/> or
/// null (<b>Nothing</b> in Visual Basic).
/// </param>
/// <param name="cvOptionValue">
/// The option value in case of using a <paramref name="cvPathPricer"/>.
/// </param>
public MonteCarloModel(PathGenerator pathGenerator,
PathPricer pathPricer, RiskStatistics sampleAccumulator,
PathPricer cvPathPricer, double cvOptionValue)
{
_pathGenerator = pathGenerator;
_pathPricer = pathPricer;
SampleAccumulator = sampleAccumulator;
_cvPathPricer = cvPathPricer;
_cvOptionValue = cvOptionValue;
}
public void TestExceptionFromStaticMethod()
{
try
{
double result = RiskStatistics.ValueAtRisk(
0.75, 0.0, 0.1);
}
catch (Exception e)
{
if (e is ArgumentException)
{
return;
}
else
{
throw e;
}
}
Assert.Fail("No exception thrown");
}
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();
}
}
}
/// <summary>
/// Free all you resources.
/// </summary>
protected void TearDown()
{
stat = null;
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(RiskStatistics 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(RiskStatistics obj) {
return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr;
}
/// <summary>
/// Initialize a new MonteCarloModel without using control variate techniques.
/// </summary>
/// <param name="pathGenerator">The <see cref="PathGenerator"/> generating single or multiple random walks.</param>
/// <param name="pathPricer">The <see cref="PathPricer"/> calculating the option price on each sample.</param>
/// <param name="sampleAccumulator">A sample accumulator to record statistics.</param>
public MonteCarloModel(PathGenerator pathGenerator,
PathPricer pathPricer, RiskStatistics sampleAccumulator)
: this(pathGenerator, pathPricer, sampleAccumulator, null, 0.0)
{
}
