AsianGBMRQMC2.java

package tutorial;
 
import java.io.IOException;
import umontreal.ssj.rng.*;
import umontreal.ssj.hups.*;
import umontreal.ssj.mcqmctools.*;
import umontreal.ssj.stat.Tally;
 
// An extension of AsianGBM2 that uses an RQMC.
public class AsianGBMRQMC2 extends AsianGBM2 {
 
   public AsianGBMRQMC2(double r, double sigma, double strike, double s0, int s, double[] zeta) {
      super(r, sigma, strike, s0, s, zeta);
   }
 
   public static void main(String[] args) throws IOException {
      int d = 12;
      double[] zeta = new double[d + 1];
      for (int j = 0; j <= d; j++)
         zeta[j] = (double) j / (double) d;
      AsianGBMRQMC2 model = new AsianGBMRQMC2(0.05, 0.5, 100.0, 100.0, d, zeta);
      Tally statMC = new Tally("value of Asian option");
      Tally statRQMC = new Tally("RQMC averages for Asian option under GBM");
      RandomStream noise = new LFSR113();
      int n = 100000; // Number of runs for MC.
      DigitalNet p = new SobolSequence(16, 31, d); // n = 2^{16} RQMC points in d dim.
      PointSetRandomization rand = new LMScrambleShift(noise);
      int m = 50; // Number of RQMC randomizations.
 
      // We first perform a Monte Carlo experiment.
      System.out.println("Ordinary MC:");
      System.out.println(MonteCarloExperiment.simulateRunsDefaultReportStudent(model, n, noise, statMC));
      System.out.println("------------------------\n");
 
      // Then we make an RQMC experiment.
      System.out.println("RQMC experiment:");
      System.out.println(RQMCExperiment.simulReplicatesRQMCDefaultReport(model, p, rand, m, statRQMC));
      System.out.println("----------------------------------------------------\n");
 
      // This single function makes both the MC and RQMC experiments and also
      // compares the variances and efficiencies.
      System.out.println("Experiment for MC/RQMC comparison:");
      System.out.println(RQMCExperiment.makeComparisonExperimentMCvsRQMC(model, noise, p, rand, n, m));
   }
}