CIRProcess.java

/*
 * Class:        CIRProcess
 * Description:  
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Universite de Montreal
 * Organization: DIRO, Universite de Montreal
 * @author       
 * @since
 *
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 */
package umontreal.ssj.stochprocess;
 
import umontreal.ssj.rng.*;
import umontreal.ssj.probdist.*;
import umontreal.ssj.randvar.*;

public class CIRProcess extends StochasticProcess {
   private RandomStream stream;
   protected ChiSquareNoncentralGen gen;
   protected double alpha, beta, sigma, nu; // number of degrees of freedom
   // Precomputed values
   protected double[] parc, parlam;

   public CIRProcess(double x0, double alpha, double b, double sigma, RandomStream stream) {
      this(x0, alpha, b, sigma, new ChiSquareNoncentralGen(stream, null));
   }

   public CIRProcess(double x0, double alpha, double b, double sigma, ChiSquareNoncentralGen gen) {
      this.alpha = alpha;
      this.beta = b;
      this.sigma = sigma;
      this.x0 = x0;
      nu = 4.0 * b * alpha / (sigma * sigma);
      this.gen = gen;
      stream = gen.getStream();
   }
 
   public double nextObservation() {
      double xOld = path[observationIndex];
      double lambda = xOld * parlam[observationIndex];
      double x;
      if (gen.getClass() == ChiSquareNoncentralPoisGen.class)
         x = parc[observationIndex] * ChiSquareNoncentralPoisGen.nextDouble(stream, nu, lambda);
      else if (gen.getClass() == ChiSquareNoncentralGamGen.class)
         x = parc[observationIndex] * ChiSquareNoncentralGamGen.nextDouble(stream, nu, lambda);
      else
         x = parc[observationIndex] * ChiSquareNoncentralGen.nextDouble(stream, nu, lambda);
      observationIndex++;
      path[observationIndex] = x;
      return x;
   }

   public double nextObservation(double nextTime) {
      double previousTime = t[observationIndex];
      double xOld = path[observationIndex];
      observationIndex++;
      t[observationIndex] = nextTime;
      double dt = nextTime - previousTime;
      double x = nextObservation(xOld, dt);
      path[observationIndex] = x;
      return x;
   }

   public double nextObservation(double x, double dt) {
      double c = -Math.expm1(-alpha * dt) * sigma * sigma / (4.0 * alpha);
      double lambda = x * Math.exp(-alpha * dt) / c;
      if (gen.getClass() == ChiSquareNoncentralPoisGen.class)
         x = c * ChiSquareNoncentralPoisGen.nextDouble(stream, nu, lambda);
      else if (gen.getClass() == ChiSquareNoncentralGamGen.class)
         x = c * ChiSquareNoncentralGamGen.nextDouble(stream, nu, lambda);
      else
         x = c * ChiSquareNoncentralGen.nextDouble(stream, nu, lambda);
      return x;
   }
 
   public double[] generatePath() {
      double xOld = x0;
      double x, lambda;
      int j;
 
      if (gen.getClass() == ChiSquareNoncentralPoisGen.class) {
         for (j = 0; j < d; j++) {
            lambda = xOld * parlam[j];
            x = parc[j] * ChiSquareNoncentralPoisGen.nextDouble(stream, nu, lambda);
            path[j + 1] = x;
            xOld = x;
         }
 
      } else if (gen.getClass() == ChiSquareNoncentralGamGen.class) {
         for (j = 0; j < d; j++) {
            lambda = xOld * parlam[j];
            x = parc[j] * ChiSquareNoncentralGamGen.nextDouble(stream, nu, lambda);
            path[j + 1] = x;
            xOld = x;
         }
 
      } else {
         for (j = 0; j < d; j++) {
            lambda = xOld * parlam[j];
            x = parc[j] * ChiSquareNoncentralGen.nextDouble(stream, nu, lambda);
            path[j + 1] = x;
            xOld = x;
         }
      }
 
      observationIndex = d;
      return path;
   }

   public double[] generatePath(RandomStream stream) {
      gen.setStream(stream);
      return generatePath();
   }

   public void setParams(double x0, double alpha, double b, double sigma) {
      this.alpha = alpha;
      this.beta = b;
      this.sigma = sigma;
      this.x0 = x0;
      nu = 4.0 * b * alpha / (sigma * sigma);
      if (observationTimesSet)
         init(); // Otherwise not needed.
   }

   public void setStream(RandomStream stream) {
      gen.setStream(stream);
   }

   public RandomStream getStream() {
      return gen.getStream();
   }

   public double getAlpha() {
      return alpha;
   }

   public double getB() {
      return beta;
   }

   public double getSigma() {
      return sigma;
   }

   public ChiSquareNoncentralGen getGen() {
      return gen;
   }
 
   protected void initArrays(int d) {
      double dt, c;
      for (int j = 0; j < d; j++) {
         dt = t[j + 1] - t[j];
         c = -Math.expm1(-alpha * dt) * sigma * sigma / (4.0 * alpha);
         parc[j] = c;
         parlam[j] = Math.exp(-alpha * dt) / c;
      }
   }
 
   // This is called by setObservationTimes to precompute constants
   // in order to speed up the path generation.
   protected void init() {
      super.init();
      parc = new double[d];
      parlam = new double[d];
      initArrays(d);
   }
 
}