public override void Initialise(SimulationGraph graph)
{
base.Initialise(graph);
var factorIdentifiers = GetFactorIdentifiers().ToArray();
_factorPaths = GetFactorIdentifiers().Select(id =>
graph.RegisterModel <MeanRevertingNormalPathModel>(id))
.ToArray();
_factors = _factorPaths.Select(p => p.Process).ToArray();
var weightsProvider = graph.Context.Data.CalibrationParametersProviderOfType <WeightsProvider>();
_factorCorrelation = new double[factorCount, factorCount];
for (int i = 0; i < factorCount; ++i)
{
for (int j = 0; j < factorCount; ++j)
{
_factorCorrelation[i, j] = NMath.Dot(
weightsProvider.Value(factorIdentifiers[i]),
weightsProvider.Value(factorIdentifiers[j]));
}
}
}
public override void Initialise(SimulationGraph graph)
{
base.Initialise(graph);
var factorIdentifiers = GetFactorIdentifiers().ToArray();
_factorPaths = GetFactorIdentifiers().Select(id =>
graph.RegisterModel<MeanRevertingNormalPathModel>(id))
.ToArray();
_factors = _factorPaths.Select(p => p.Process).ToArray();
var weightsProvider = graph.Context.Data.CalibrationParametersProviderOfType<WeightsProvider>();
_factorCorrelation = new double[factorCount, factorCount];
for (int i = 0; i < factorCount; ++i)
{
for (int j = 0; j < factorCount; ++j)
{
_factorCorrelation[i, j] = NMath.Dot(
weightsProvider.Value(factorIdentifiers[i]),
weightsProvider.Value(factorIdentifiers[j]));
}
}
}
public override void Initialise(SimulationGraph graph)
{
// we get the (single) instance of the MultiVariateNormalModel
var model = graph.RegisterModel <MultiVariateNormalModel>("General");
// and store the factor
_factor = model.AddFactor(Identifier, graph);
}
public override void Initialise(SimulationGraph graph)
{
base.Initialise(graph);
_state = new NArray[graph.Context.Settings.SimulationTimePoints.Length];
_weinerPaths = GetFactorIdentifiers().Select(id =>
graph.RegisterModel <WienerPathModel>(id))
.ToArray();
}
public override void Initialise(SimulationGraph graph)
{
base.Initialise(graph);
_state = new NArray[graph.Context.Settings.SimulationTimePoints.Length];
_weinerPaths = GetFactorIdentifiers().Select(id =>
graph.RegisterModel<WienerPathModel>(id))
.ToArray();
}
/// <summary>
/// Simulate state variables needed for the pricing step of the exposure calculation. State variables
/// are not independent variables of the adjoint algorithmic differentation, and therefore can be simulated in advance.
/// </summary>
/// <param name="model"></param>
/// <param name="allZeroRatesT0"></param>
/// <param name="timePoints"></param>
/// <param name="graph"></param>
/// <param name="pricers"></param>
public void SimulateModel(out LinearGaussianModel model, out IEnumerable<NArray> allZeroRatesT0,
out TimePoint[] timePoints, out SimulationGraph graph, out List<IPricer> pricers)
{
var testDate = new DateTime(2015, 12, 1);
graph = new SimulationGraph(StorageLocation.Host, testDate);
var context = graph.Context;
context.Settings.SimulationCount = 5000;
var fixedLeg = Enumerable.Range(0, 40).Select(i => new FixedCashflowDeal()
{
Notional = -1e6,
Rate = 0.002,
Currency = Currency.EUR,
StartDate = testDate.AddMonths(3 * i),
EndDate = testDate.AddMonths(3 * (i + 1))
});
var floatingLeg = Enumerable.Range(0, 40).Select(i => new FloatingCashflowDeal()
{
Notional = 1e6,
Currency = Currency.EUR,
StartDate = testDate.AddMonths(3 * i),
EndDate = testDate.AddMonths(3 * (i + 1))
});
var correlationMatrix = context.Factory.CreateNArray(new double[,] {
{ 1.0, -0.92, 0.5},
{ -0.92, 1.0, -0.8},
{ 0.5, -0.8, 1.0}
});
var identifiers = new string[] { "IR_DiscountFactor_EUR_Factor0", "IR_DiscountFactor_EUR_Factor1",
"IR_DiscountFactor_EUR_Factor2"};
CorrelationHelper.AddMultivariateModelWeightsProvider(context, identifiers, correlationMatrix);
pricers = fixedLeg.Select(d => new FixedCashflowPricer(d) as IPricer)
.Concat(floatingLeg.Select(d => new FloatingCashflowPricer(d) as IPricer)).ToList();
foreach (var pricer in pricers) pricer.Register(graph);
var testVariates0 = graph.RegisterFactor<NormalVariates>("IR_DiscountFactor_EUR_Factor0");
var testVariates1 = graph.RegisterFactor<NormalVariates>("IR_DiscountFactor_EUR_Factor1");
var testVariates2 = graph.RegisterFactor<NormalVariates>("IR_DiscountFactor_EUR_Factor2");
var testFactor = graph.RegisterModel<MeanRevertingNormalPathModel>("IR_DiscountFactor_EUR_Factor0");
model = graph.RegisterModel<LinearGaussianModel>("EUR");
var numeraire = graph.RegisterModel<NumeraireModel>("EUR");
model.Factors[0].Sigma = 0.02; model.Factors[0].Lambda = 0.05;
model.Factors[1].Sigma = 0.03; model.Factors[1].Lambda = 0.2;
model.Factors[2].Sigma = 0.01; model.Factors[2].Lambda = 1.0;
var years = new double[] { 0, 1/365.35, 7/365.25, 14/365.25, 1/12, 2/12, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 };
var zeroRatesInPercent = new double[] { -0.3, -0.3, -0.29, -0.29, -0.3, -0.31, -0.31, -0.32, -0.32, -0.33, -0.34, -0.35, -0.35, -0.33, -0.28, -0.2, -0.11, -0.01, 0.1, 0.2, 0.37, 0.56, 0.71, 0.75, 0.76, 0.75, 0.75, 0.72, 0.7, 0.67, 0.64 };
model.ZeroRatesT0 = new Curve(years.Zip(zeroRatesInPercent,
(y, r) => new DataPoint(
testDate.AddDays(y * 365.25),
NArray.CreateScalar(r / 100)
)).ToArray());
var runner = graph.ToSimulationRunner();
runner.Prepare();
runner.Simulate();
timePoints = graph.Context.Settings.SimulationTimePoints;
allZeroRatesT0 = model.ZeroRatesT0.Data.Select(d => d.Value);
}
/// <summary>
/// Simulate state variables needed for the pricing step of the exposure calculation. State variables
/// are not independent variables of the adjoint algorithmic differentation, and therefore can be simulated in advance.
/// </summary>
/// <param name="model"></param>
/// <param name="allZeroRatesT0"></param>
/// <param name="timePoints"></param>
/// <param name="graph"></param>
/// <param name="pricers"></param>
public void SimulateModel(out LinearGaussianModel model, out IEnumerable <NArray> allZeroRatesT0,
out TimePoint[] timePoints, out SimulationGraph graph, out List <IPricer> pricers)
{
var testDate = new DateTime(2015, 12, 1);
graph = new SimulationGraph(StorageLocation.Host, testDate);
var context = graph.Context;
context.Settings.SimulationCount = 5000;
var fixedLeg = Enumerable.Range(0, 40).Select(i => new FixedCashflowDeal()
{
Notional = -1e6,
Rate = 0.002,
Currency = Currency.EUR,
StartDate = testDate.AddMonths(3 * i),
EndDate = testDate.AddMonths(3 * (i + 1))
});
var floatingLeg = Enumerable.Range(0, 40).Select(i => new FloatingCashflowDeal()
{
Notional = 1e6,
Currency = Currency.EUR,
StartDate = testDate.AddMonths(3 * i),
EndDate = testDate.AddMonths(3 * (i + 1))
});
var correlationMatrix = context.Factory.CreateNArray(new double[, ] {
{ 1.0, -0.92, 0.5 },
{ -0.92, 1.0, -0.8 },
{ 0.5, -0.8, 1.0 }
});
var identifiers = new string[] { "IR_DiscountFactor_EUR_Factor0", "IR_DiscountFactor_EUR_Factor1",
"IR_DiscountFactor_EUR_Factor2" };
CorrelationHelper.AddMultivariateModelWeightsProvider(context, identifiers, correlationMatrix);
pricers = fixedLeg.Select(d => new FixedCashflowPricer(d) as IPricer)
.Concat(floatingLeg.Select(d => new FloatingCashflowPricer(d) as IPricer)).ToList();
foreach (var pricer in pricers)
{
pricer.Register(graph);
}
var testVariates0 = graph.RegisterFactor <NormalVariates>("IR_DiscountFactor_EUR_Factor0");
var testVariates1 = graph.RegisterFactor <NormalVariates>("IR_DiscountFactor_EUR_Factor1");
var testVariates2 = graph.RegisterFactor <NormalVariates>("IR_DiscountFactor_EUR_Factor2");
var testFactor = graph.RegisterModel <MeanRevertingNormalPathModel>("IR_DiscountFactor_EUR_Factor0");
model = graph.RegisterModel <LinearGaussianModel>("EUR");
var numeraire = graph.RegisterModel <NumeraireModel>("EUR");
model.Factors[0].Sigma = 0.02; model.Factors[0].Lambda = 0.05;
model.Factors[1].Sigma = 0.03; model.Factors[1].Lambda = 0.2;
model.Factors[2].Sigma = 0.01; model.Factors[2].Lambda = 1.0;
var years = new double[] { 0, 1 / 365.35, 7 / 365.25, 14 / 365.25, 1 / 12, 2 / 12, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 };
var zeroRatesInPercent = new double[] { -0.3, -0.3, -0.29, -0.29, -0.3, -0.31, -0.31, -0.32, -0.32, -0.33, -0.34, -0.35, -0.35, -0.33, -0.28, -0.2, -0.11, -0.01, 0.1, 0.2, 0.37, 0.56, 0.71, 0.75, 0.76, 0.75, 0.75, 0.72, 0.7, 0.67, 0.64 };
model.ZeroRatesT0 = new Curve(years.Zip(zeroRatesInPercent,
(y, r) => new DataPoint(
testDate.AddDays(y * 365.25),
NArray.CreateScalar(r / 100)
)).ToArray());
var runner = graph.ToSimulationRunner();
runner.Prepare();
runner.Simulate();
timePoints = graph.Context.Settings.SimulationTimePoints;
allZeroRatesT0 = model.ZeroRatesT0.Data.Select(d => d.Value);
}