// ProcessArr 생성용 //외부사용함.
public static StochasticProcessArray buildProcess(UnderlyingInfo underInfo)
{
Date today = Settings.evaluationDate();
List<Handle<Quote>> underlyingH = new List<Handle<Quote>>();
// bootstrap the yield/dividend/vol curves
CorrelationSetting corrSetting = new CorrelationSetting();
List<Underlying> underlyings = underInfo.underlyings();
Matrix CorrMatrix = corrSetting.correlationMatrix(today);
List<StochasticProcess1D> process1DList = new List<StochasticProcess1D>();
for (int i = 0; i < underlyings.Count ; i++)
{
process1DList.Add(ProcessFactory.buildProcess1D(underlyings[i]));
}
StochasticProcessArray processArr = new StochasticProcessArray(process1DList, CorrMatrix);
return processArr;
}
public void testMultiPathGenerator()
{
//("Testing n-D path generation against cached values...");
//SavedSettings backup;
Settings.setEvaluationDate(new Date(26,4,2005));
Handle<Quote> x0=new Handle<Quote> (new SimpleQuote(100.0));
Handle<YieldTermStructure> r =new Handle<YieldTermStructure> (Utilities.flatRate(0.05, new Actual360()));
Handle<YieldTermStructure> q=new Handle<YieldTermStructure> (Utilities.flatRate(0.02, new Actual360()));
Handle<BlackVolTermStructure> sigma=new Handle<BlackVolTermStructure> (Utilities.flatVol(0.20, new Actual360()));
Matrix correlation=new Matrix(3,3);
correlation[0,0] = 1.0; correlation[0,1] = 0.9; correlation[0,2] = 0.7;
correlation[1,0] = 0.9; correlation[1,1] = 1.0; correlation[1,2] = 0.4;
correlation[2,0] = 0.7; correlation[2,1] = 0.4; correlation[2,2] = 1.0;
List<StochasticProcess1D> processes = new List<StochasticProcess1D>(3);
StochasticProcess process;
processes.Add(new BlackScholesMertonProcess(x0,q,r,sigma));
processes.Add(new BlackScholesMertonProcess(x0,q,r,sigma));
processes.Add(new BlackScholesMertonProcess(x0,q,r,sigma));
process = new StochasticProcessArray(processes,correlation);
// commented values must be used when Halley's correction is enabled
double[] result1 = {
188.2235868185,
270.6713069569,
113.0431145652 };
// Real result1[] = {
// 188.2235869273,
// 270.6713071508,
// 113.0431145652 };
double[] result1a = {
64.89105742957,
45.12494404804,
108.0475146914 };
// Real result1a[] = {
// 64.89105739157,
// 45.12494401537,
// 108.0475146914 };
testMultiple(process, "Black-Scholes", result1, result1a);
processes[0] = new GeometricBrownianMotionProcess(100.0, 0.03, 0.20);
processes[1] = new GeometricBrownianMotionProcess(100.0, 0.03, 0.20);
processes[2] = new GeometricBrownianMotionProcess(100.0, 0.03, 0.20);
process = new StochasticProcessArray(processes,correlation);
double[] result2 = {
174.8266131680,
237.2692443633,
119.1168555440 };
// Real result2[] = {
// 174.8266132344,
// 237.2692444869,
// 119.1168555605 };
double[] result2a = {
57.69082393020,
38.50016862915,
116.4056510107 };
// Real result2a[] = {
// 57.69082387657,
// 38.50016858691,
// 116.4056510107 };
testMultiple(process, "geometric Brownian", result2, result2a);
processes[0] = new OrnsteinUhlenbeckProcess(0.1, 0.20);
processes[1] = new OrnsteinUhlenbeckProcess(0.1, 0.20);
processes[2] = new OrnsteinUhlenbeckProcess(0.1, 0.20);
process = new StochasticProcessArray(processes,correlation);
double[] result3 = {
0.2942058437284,
0.5525006418386,
0.02650931054575 };
double[] result3a = {
-0.2942058437284,
-0.5525006418386,
-0.02650931054575 };
testMultiple(process, "Ornstein-Uhlenbeck", result3, result3a);
processes[0] = new SquareRootProcess(0.1, 0.1, 0.20, 10.0);
processes[1] = new SquareRootProcess(0.1, 0.1, 0.20, 10.0);
processes[2] = new SquareRootProcess(0.1, 0.1, 0.20, 10.0);
process = new StochasticProcessArray(processes,correlation);
double[] result4 = {
4.279510844897,
4.943783503533,
3.590930385958 };
double[] result4a = {
2.763967737724,
2.226487196647,
3.503859264341 };
testMultiple(process, "square-root", result4, result4a);
}
public void setUnderlying(UnderlyingInfo under)
{
this.processArr_ = ProcessFactory.buildProcess(under);
this.pathGenerator(under);
}
