Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.op()

Package org.jquantlib.math.distributions

Class org.jquantlib.math.distributions.CumulativeNormalDistribution

Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.op()

org.jquantlib.math.distributions.CumulativeNormalDistribution.op()
Computes the cumulative normal distribution.
Asymptotic expansion for very negative z as references on M. Abramowitz book. @see cite: "Monte Carlo Methods in Finance", ISBN-13: 978-0471497417 @see cite: M. Abramowitz and A. Stegun, Pocketbook of Mathematical Functions, ISBN 3-87144818-4, p.408, item 26.2.12 @param z @return result

            }
            break;
        case Put:
            Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = payoff.strike() - Si;
            RHS = temp - (1 - dividendDiscount * cumNormalDist.op(-d1)) * Si / Q;
            bi = -dividendDiscount * cumNormalDist.op(-d1) * (1 - 1/Q)
            - (1 + dividendDiscount * cumNormalDist.derivative(-d1) / Math.sqrt(variance)) / Q;
            while (Math.abs(LHS - RHS)/payoff.strike() > tolerance) {
                Si = (payoff.strike() - RHS + bi * Si) / (1 + bi);
                forwardSi = Si * dividendDiscount / riskFreeDiscount;

View Full Code Here

            break;
        case Put:
            Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = payoff.strike() - Si;
            RHS = temp - (1 - dividendDiscount * cumNormalDist.op(-d1)) * Si / Q;
            bi = -dividendDiscount * cumNormalDist.op(-d1) * (1 - 1/Q)
            - (1 + dividendDiscount * cumNormalDist.derivative(-d1) / Math.sqrt(variance)) / Q;
            while (Math.abs(LHS - RHS)/payoff.strike() > tolerance) {
                Si = (payoff.strike() - RHS + bi * Si) / (1 + bi);
                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);

View Full Code Here

                Si = (payoff.strike() - RHS + bi * Si) / (1 + bi);
                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = payoff.strike() - Si;
                final double /*@Real*/ temp2 = BlackFormula.blackFormula(payoff.optionType(), payoff.strike(), forwardSi, Math.sqrt(variance))*riskFreeDiscount;
                RHS = temp2 - (1 - dividendDiscount * cumNormalDist.op(-d1)) * Si / Q;
                bi = -dividendDiscount * cumNormalDist.op(-d1) * (1 - 1 / Q)
                - (1 + dividendDiscount * cumNormalDist.derivative(-d1) / Math.sqrt(variance)) / Q;
            }
            break;
        default:

View Full Code Here

                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = payoff.strike() - Si;
                final double /*@Real*/ temp2 = BlackFormula.blackFormula(payoff.optionType(), payoff.strike(), forwardSi, Math.sqrt(variance))*riskFreeDiscount;
                RHS = temp2 - (1 - dividendDiscount * cumNormalDist.op(-d1)) * Si / Q;
                bi = -dividendDiscount * cumNormalDist.op(-d1) * (1 - 1 / Q)
                - (1 + dividendDiscount * cumNormalDist.derivative(-d1) / Math.sqrt(variance)) / Q;
            }
            break;
        default:
            throw new LibraryException(UNKNOWN_OPTION_TYPE); // QA:[RG]::verified

View Full Code Here

            final double /* @Real */hA = phi * (Sk - payoff.strike()) - black_Sk;


            final double /* @Real */d1_Sk = (Math.log(forwardSk / payoff.strike()) + 0.5 * variance) / Math.sqrt(variance);
            final double /* @Real */d2_Sk = d1_Sk - Math.sqrt(variance);
            final double /* @Real */part1 = forwardSk * normalDist.op(d1_Sk) / (alpha * Math.sqrt(variance));
            final double /* @Real */part2 = -phi * forwardSk * cumNormalDist.op(phi * d1_Sk) * Math.log(dividendDiscount) / Math.log(riskFreeDiscount);
            final double /* @Real */part3 = +phi * payoff.strike() * cumNormalDist.op(phi * d2_Sk);
            final double /* @Real */V_E_h = part1 + part2 + part3;


            final double /* @Real */b = (1 - h) * alpha * lambda_prime / (2 * (2 * lambda + beta - 1));
            final double /* @Real */c = -((1 - h) * alpha / (2 * lambda + beta - 1)) * (V_E_h / (hA) + 1 / h + lambda_prime / (2 * lambda + beta - 1));

View Full Code Here


            final double /* @Real */d1_Sk = (Math.log(forwardSk / payoff.strike()) + 0.5 * variance) / Math.sqrt(variance);
            final double /* @Real */d2_Sk = d1_Sk - Math.sqrt(variance);
            final double /* @Real */part1 = forwardSk * normalDist.op(d1_Sk) / (alpha * Math.sqrt(variance));
            final double /* @Real */part2 = -phi * forwardSk * cumNormalDist.op(phi * d1_Sk) * Math.log(dividendDiscount) / Math.log(riskFreeDiscount);
            final double /* @Real */part3 = +phi * payoff.strike() * cumNormalDist.op(phi * d2_Sk);
            final double /* @Real */V_E_h = part1 + part2 + part3;


            final double /* @Real */b = (1 - h) * alpha * lambda_prime / (2 * (2 * lambda + beta - 1));
            final double /* @Real */c = -((1 - h) * alpha / (2 * lambda + beta - 1)) * (V_E_h / (hA) + 1 / h + lambda_prime / (2 * lambda + beta - 1));
            final double /* @Real */temp_spot_ratio = Math.log(spot / Sk);

View Full Code Here

            }


            final double /* @Real */temp_chi_prime = (2 * b / spot) * Math.log(spot / Sk);
            final double /* @Real */chi_prime = temp_chi_prime + c / spot;
            final double /* @Real */chi_double_prime = 2 * b / (spot * spot) - temp_chi_prime / spot - c / (spot * spot);
            greeks.delta = phi * dividendDiscount * cumNormalDist.op(phi * d1_Sk)
            + (lambda / (spot * (1 - chi)) + chi_prime / ((1 - chi)*(1 - chi))) *
            (phi * (Sk - payoff.strike()) - black_Sk) * Math.pow((spot/Sk), lambda);


            greeks.gamma = phi * dividendDiscount * normalDist.op(phi*d1_Sk) /
            (spot * Math.sqrt(variance))

View Full Code Here

    final CumulativeNormalDistribution cnd = new CumulativeNormalDistribution();


    for (final double[] testvalue : testvalues) {
      final double z = testvalue[0];
      final double expected = testvalue[1];
      final double computed = cnd.op(z);
      final double tolerance = (Math.abs(z)<3.01) ? 1.0e-15: 1.0e-10;




      // assertEquals(expected, computed,tolerance);
      if(expected - computed > tolerance){

View Full Code Here

      if(expected - computed > tolerance){
        fail("expected:  + " + expected + " but was " + computed);
      }


      // assertEquals(1.0, computed+ cnd.evaluate(-z),tolerance);
      if (Math.abs(1.0-(computed+cnd.op(-z)))>tolerance) {
        fail("expected: 1.0" + " but is: " + computed + cnd.op(-z));
      }
    }
  }

View Full Code Here

        fail("expected:  + " + expected + " but was " + computed);
      }


      // assertEquals(1.0, computed+ cnd.evaluate(-z),tolerance);
      if (Math.abs(1.0-(computed+cnd.op(-z)))>tolerance) {
        fail("expected: 1.0" + " but is: " + computed + cnd.op(-z));
      }
    }
  }


  @Test

View Full Code Here

0 1 2 3 4 5 6

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.