/// <summary>
/// Format the y parameter so it can be made compatible with <see cref="Pelsser.Bond"/>.
/// </summary>
/// <param name="y">The y parameter wanted inside Bond.</param>
/// <param name="process">The Pelsser process which will be used.</param>
/// <returns>The Matrix to pass to Bond.</returns>
private Matrix DynamicParam(double y, SquaredGaussianModel process)
{
double alphaT = process.F(process.CacheDates[0], process.CacheDates[1] - process.CacheDates[0]) +
2 * Math.Exp(-process.a1.V() * process.CacheDates[0]) * process.Int(0, process.CacheDates[0]);
return(new Matrix(new double[] { Math.Pow(y + alphaT, 2) }));
}
public static void Calculate(double t1, double t2, double simEnd, int numSim, int numSteps, out double val, out double stDev)
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
// To be changed to 350000.
int n_sim = numSim;
int n_steps = numSteps;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = 0.0;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(" + t1.ToString() + ";" + t2.ToString() + ";@v1)";
// Here we put the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)simEnd;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
ResultItem price = rov.m_ResultList[0] as ResultItem;
val = price.value;
stDev = price.stdDev / Math.Sqrt((double)numSim);
}
public void Test()
{
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)"0.05";
rov.Symbols.Add(zerorate);
int n_sim = 5000;
int n_steps = 900;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.0704877770928728;
process.sigma1 = (ModelParameter)0.116251516678772;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(t;10;@v1)";
// Set the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)2.0;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
}
public void Test()
{
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)"0.05";
rov.Symbols.Add(zerorate);
int n_sim = 5000;
int n_steps = 900;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.0704877770928728;
process.sigma1 = (ModelParameter)0.116251516678772;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(t;10;@v1)";
// Set the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)2.0;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
}
/// <summary>
/// Constructor to create a new instance of the PelsserCache.
/// </summary>
/// <param name="t">The first date parameter this element is referenced to.</param>
/// <param name="s">The second date parameter this element is referenced to.</param>
/// <param name="p_instance">
/// The instance to the <see cref="SquaredGaussianModel"/> this cache references to.
/// </param>
public PelsserCache(double t, double s, SquaredGaussianModel p_instance)
{
this.instance = p_instance;
double[] btT;
// Integral calculation is always done with daily intervals.
int ipy = 252;
double cached_dt = this.instance.CacheDates[1] - this.instance.CacheDates[0];
// Indices representing t and s on the mDates discretization.
if (this.instance.CacheDates[this.instance.CacheDates.Length - 1] < s || 1.0 / cached_dt < ipy)
{
double dt = 1.0 / ipy;
double[] newDates = new double[(int)(1 + s * ipy)];
for (int j = 0; j < newDates.Length; j++)
{
newDates[j] = j * dt;
}
this.instance.CalculateValueForCache(newDates);
}
int ti = DVPLDOM.AdaptiveTimeDiscretization.DiscreteTime(t, this.instance.CacheDates);
int si = DVPLDOM.AdaptiveTimeDiscretization.DiscreteTime(s, this.instance.CacheDates);
double delta = this.instance.CacheDates[1] - this.instance.CacheDates[0];
btT = this.instance.B(ti, si, delta);
this.A = this.instance.A(ti, si, delta, btT);
if (btT.Length > 0)
{
this.B = btT[0];
}
this.CtT0 = this.instance.C(s - t);
}
/// <summary>
/// Caplet prices calculated as a put on a zero coupon bond.
/// </summary>
/// <param name="model">The model to use to execute the calculation.</param>
/// <param name="mat">
/// Caplet maturity. This vector starts from zero and increases
/// of step DeltaK each element till the last one.
/// </param>
/// <param name="fwd">Forward with the deltaK step.</param>
/// <param name="rk">Strike vector (columns).</param>
/// <param name="deltaK">Amount to use as increase factor.</param>
/// <param name="tOss">The Maturities.</param>
/// <returns>A <see cref="Matrix"/> with the caplet prices.</returns>
public Matrix PGSMCaplets(SquaredGaussianModel model, Vector mat, Vector fwd, Vector rk, double deltaK, Vector tOss)
{
double s = model.sigma1.fV();
int col = rk.Length;
int NP = (int)(1 + tOss[tOss.Length - 1] * 252);
double[] dates = new double[NP];
double step = mat[mat.Length - 1] / (NP - 1);
for (int z = 0; z < NP; z++)
dates[z] = step * z;
DateTime t0 = DateTime.Now;
model.Setup(dates);
DateTime t1 = DateTime.Now;
Vector K = 1.0 / (1 + rk * deltaK);
double cCost = model.C(deltaK);
Vector sigma0s = Math.Pow(s, 2) * CtT(model, 0, mat);
Matrix caplets = new Matrix(mat.Length - 1, rk.Length);
Matrix caps = new Matrix(tOss.Length, rk.Length);
// Pre-calculate values.
Vector logK = Vector.Log(K);
// Parallel version.
List<Task> tl = new List<Task>();
Context context = new Context();
context.Model = model;
context.Mat = mat;
context.Fwd = fwd;
context.RK = rk;
context.DeltaK = deltaK;
context.TOss = tOss;
context.K = K;
context.LogK = logK;
context.Caplets = caplets;
context.Sigma0s = sigma0s;
context.CCost = cCost;
context.RowStart = 0;
context.RowEnd = (mat.Length - 2) / 2;
tl.Add(Task.Factory.StartNew(Context.CalculateRowP, context));
context = new Context();
context.Model = model;
context.Mat = mat;
context.Fwd = fwd;
context.RK = rk;
context.DeltaK = deltaK;
context.TOss = tOss;
context.K = K;
context.LogK = logK;
context.Caplets = caplets;
context.Sigma0s = sigma0s;
context.CCost = cCost;
context.RowStart = (mat.Length - 2) / 2 + 1;
context.RowEnd = mat.Length - 2 - 1;
tl.Add(Task.Factory.StartNew(Context.CalculateRowP, context));
tsScheduler.WaitTaskList(tl);
// Sequential version.
/*
Context Context = new Context();
Context.Model = Model;
Context.Mat = Mat;
Context.Fwd = Fwd;
Context.RK = RK;
Context.DeltaK = DeltaK;
Context.TOss = TOss;
Context.K = K;
Context.LogK = LogK;
Context.Caplets = Caplets;
Context.Sigma0s = Sigma0s;
Context.CCost = CCost;
for (int r = 0; r < Mat.Length - 2; r++)
Context.CalculateRow(r);
*/
// Calculate the caps from the caplets.
for (int r = 0; r < tOss.Length; r++)
{
for (int c = 0; c < rk.Length; c++)
{
double current = 0;
for (int ci = 0; ci < (int)(tOss[r] / deltaK) - 1; ci++)
current += caplets[ci, c];
caps[r, c] = current;
}
}
return caps;
}
/// <summary>
/// Calculates the integral C(t, T) function with T taking
/// every element of the parameter mat.
/// </summary>
/// <param name="model">The model instance.</param>
/// <param name="t">The starting point of the integral.</param>
/// <param name="mat">The vector of ending interval points T.</param>
/// <returns>The vector of pre-calculated C(t,T).</returns>
private Vector CtT(SquaredGaussianModel model, double t, Vector mat)
{
Vector C = new Vector(mat.Length);
for (int i = 0; i < mat.Length; i++)
C[i] = model.C(mat[i] - t);
return C;
}
/// <summary>
/// Constructor to create a new instance of the PelsserCache.
/// </summary>
/// <param name="t">The first date parameter this element is referenced to.</param>
/// <param name="s">The second date parameter this element is referenced to.</param>
/// <param name="p_instance">
/// The instance to the <see cref="SquaredGaussianModel"/> this cache references to.
/// </param>
public PelsserCache(double t, double s, SquaredGaussianModel p_instance)
{
this.instance = p_instance;
double[] btT;
// Integral calculation is always done with daily intervals.
int ipy = 252;
double cached_dt = this.instance.CacheDates[1] - this.instance.CacheDates[0];
// Indices representing t and s on the mDates discretization.
if (this.instance.CacheDates[this.instance.CacheDates.Length - 1] < s || 1.0 / cached_dt < ipy)
{
double dt = 1.0 / ipy;
double[] newDates = new double[(int)(1 + s * ipy)];
for (int j = 0; j < newDates.Length; j++)
newDates[j] = j * dt;
this.instance.CalculateValueForCache(newDates);
}
int ti = DVPLDOM.AdaptiveTimeDiscretization.DiscreteTime(t, this.instance.CacheDates);
int si = DVPLDOM.AdaptiveTimeDiscretization.DiscreteTime(s, this.instance.CacheDates);
double delta = this.instance.CacheDates[1] - this.instance.CacheDates[0];
btT = this.instance.B(ti, si, delta);
this.A = this.instance.A(ti, si, delta, btT);
if (btT.Length > 0)
{
this.B = btT[0];
}
this.CtT0 = this.instance.C(s - t);
}
/// <summary>
/// Attempts a calibration through <see cref="PelsserCappletOptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
EstimationResult result;
if ((dataset.ZRMarket == null) || (dataset.CapVolatility == null))
{
result = new EstimationResult();
result.ErrorMessage = "Not enough data to calibrate.\n" +
"The estimator needs a ZRMarket and a CapVolatility " +
"defined inside InterestRateMarketData";
return result;
}
// Backup the dates
DateTime effectiveDate = DateTime.Now.Date;
DateTime valuationDate = DateTime.Now.Date;
if (Document.ActiveDocument != null)
{
effectiveDate = Document.ActiveDocument.ContractDate;
valuationDate = Document.ActiveDocument.SimulationStartDate;
}
// Creates the Context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
Function zr = new PFunction(null);
zr.VarName = "zr";
// Load the zr.
double[,] zrvalue = (double[,])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
prj.Symbols.Add(zr);
BlackModel bm = new BlackModel(zr);
double deltak = dataset.CapTenor;
Matrix capVol = dataset.CapVolatility;
Vector capMat = dataset.CapMaturity;
Vector capK = dataset.CapRate;
var preferences = settings as Fairmat.Calibration.CapVolatilityFiltering;
// Matrix calculated with black.
Matrix blackCaps = new Matrix(capMat.Length, capK.Length);
for (int m = 0; m < capMat.Length; m++)
{
for (int s = 0; s < capK.Length; s++)
{
bool skip = false;
if (preferences != null)
{
if (capK[s] < preferences.MinCapRate || capK[s] > preferences.MaxCapRate ||
capMat[m] < preferences.MinCapMaturity || capMat[m] > preferences.MaxCapMaturity)
{skip = true; }
}
if (capVol[m, s] == 0 || skip)
blackCaps[m, s] = 0;
else
blackCaps[m, s] = bm.Cap(capK[s], capVol[m, s], deltak, capMat[m]);
}
}
if (blackCaps.IsNAN())
{
Console.WriteLine("Black caps matrix has non real values:");
Console.WriteLine(blackCaps);
throw new Exception("Cannot calculate Black caps");
}
// Maturity goes from 0 to the last item with step deltaK.
Vector maturity = new Vector((int)(1.0 + capMat[capMat.Length - 1] / deltak));
for (int l = 0; l < maturity.Length; l++)
maturity[l] = deltak * l;
Vector fwd = new Vector(maturity.Length - 1);
for (int i = 0; i < fwd.Length; i++)
{
fwd[i] = bm.Fk(maturity[i + 1], deltak);
}
// Creates a default Pelsser model.
Pelsser.SquaredGaussianModel model = new Pelsser.SquaredGaussianModel();
model.a1 = (ModelParameter)0.014;
model.sigma1 = (ModelParameter)0.001;
model.zr = (ModelParameter)"@zr";
StochasticProcessExtendible iex = new StochasticProcessExtendible(prj, model);
prj.Processes.AddProcess(iex);
prj.Parse();
DateTime t0 = DateTime.Now;
Caplet cp = new Caplet();
PelsserCappletOptimizationProblem problem = new PelsserCappletOptimizationProblem(prj, cp, maturity, fwd, capK, deltak, capMat, blackCaps);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 35;
o.TargetCost = 0.0025;
o.MaxIter = 10;
o.Verbosity = Math.Max(1, Engine.Verbose);
o.controller = controller;
// Parallel evaluation is not supported for this calibration.
o.Parallel = false;
o.Debug = true;
SolutionInfo solution = null;
Vector x0 = (Vector)new double[] { 0.1, 0.1 };
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
o.epsilon = 10e-7;
o.h = 10e-7;
o.MaxIter = 1000;
o.Debug = true;
o.Verbosity = Math.Max(1, Engine.Verbose);
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
solution = solver2.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine(solution);
string[] names = new string[] { "alpha1", "sigma1" };
result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
result.Objects = new object[1];
result.Objects[0] = solution.obj;
//result.Fit = solution.obj;//Uncomment in 1.6
// Restore the dates
if (Document.ActiveDocument != null)
{
Document.ActiveDocument.ContractDate = effectiveDate;
Document.ActiveDocument.SimulationStartDate = valuationDate;
}
return result;
}
/// <summary>
/// Format the y parameter so it can be made compatible with <see cref="Pelsser.Bond"/>.
/// </summary>
/// <param name="y">The y parameter wanted inside Bond.</param>
/// <param name="process">The Pelsser process which will be used.</param>
/// <returns>The Matrix to pass to Bond.</returns>
private Matrix DynamicParam(double y, SquaredGaussianModel process)
{
double alphaT = process.F(process.CacheDates[0], process.CacheDates[1] - process.CacheDates[0]) +
2 * Math.Exp(-process.a1.V() * process.CacheDates[0]) * process.Int(0, process.CacheDates[0]);
return new Matrix(new double[] { Math.Pow(y + alphaT, 2) });
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
int n_sim = 5000;
int n_steps = 900;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = 0.0;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(t;10;@v1)";
// Set the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)2.0;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
Console.WriteLine("Bond Test Value = " + price.value.ToString());
Assert.LessOrEqual(Math.Abs(0.6702 - price.value), .01);
// Try to do some simple tests and check the results.
double b0_10 = process.Bond(DynamicParam(0, process), process.CacheDates, 0, 0, 10);
Console.WriteLine("Bond(0,10) = " + b0_10);
Assert.LessOrEqual(Math.Abs(b0_10 - 0.606513), .001);
double b7_10 = process.Bond(DynamicParam(0.00427631, process), process.CacheDates, 0, 7, 10);
Console.WriteLine("Bond(7,10) = " + b7_10);
Assert.LessOrEqual(Math.Abs(b7_10 - 0.856374), .001);
double b7_30 = process.Bond(DynamicParam(0.00427631, process), process.CacheDates, 0, 7, 30);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
int n_sim = 5000;
int n_steps = 900;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = 0.0;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(t;10;@v1)";
// Set the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)2.0;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
Console.WriteLine("Bond Test Value = " + price.value.ToString());
Assert.LessOrEqual(Math.Abs(0.6702 - price.value), .01);
// Try to do some simple tests and check the results.
double b0_10 = process.Bond(DynamicParam(0, process), process.CacheDates, 0, 0, 10);
Console.WriteLine("Bond(0,10) = " + b0_10);
Assert.LessOrEqual(Math.Abs(b0_10 - 0.606513), .001);
double b7_10 = process.Bond(DynamicParam(0.00427631, process), process.CacheDates, 0, 7, 10);
Console.WriteLine("Bond(7,10) = " + b7_10);
Assert.LessOrEqual(Math.Abs(b7_10 - 0.856374), .001);
double b7_30 = process.Bond(DynamicParam(0.00427631, process), process.CacheDates, 0, 7, 30);
}
public static void Calculate(double t1, double t2, double simEnd, int numSim, int numSteps, out double val, out double stDev)
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
// To be changed to 350000.
int n_sim = numSim;
int n_steps = numSteps;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = 0.0;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "bond(" + t1.ToString() + ";" + t2.ToString() + ";@v1)";
// Here we put the simulation maturity.
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)simEnd;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
ResultItem price = rov.m_ResultList[0] as ResultItem;
val = price.value;
stDev = price.stdDev / Math.Sqrt((double)numSim);
}