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

        if (variance >= Math.E) {
            D1 = log_H_S / stdDev + mu * stdDev;
            D2 = D1 - 2.0 * mu * stdDev;
            final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
            cum_d1 = f.op(D1);
            cum_d2 = f.op(D2);
            n_d1 = f.derivative(D1);
            n_d2 = f.derivative(D2);
        } else {
            if (log_H_S > 0) {
                cum_d1 = 1.0;

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                cum_d2 = 1.0;
            } else {
                D1 = Math.log(forward / strike) / stdDev + 0.5 * stdDev;
                D2 = D1 - stdDev;
                final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
                cum_d1 = f.op(D1);
                cum_d2 = f.op(D2);
                n_d1 = f.derivative(D1);
                n_d2 = f.derivative(D2);
            }
        } else {

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            } else {
                D1 = Math.log(forward / strike) / stdDev + 0.5 * stdDev;
                D2 = D1 - stdDev;
                final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
                cum_d1 = f.op(D1);
                cum_d2 = f.op(D2);
                n_d1 = f.derivative(D1);
                n_d2 = f.derivative(D2);
            }
        } else {
            if (forward > strike) {

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        final CumulativeNormalDistribution CND = new CumulativeNormalDistribution();
        final NormalDistribution ND = new NormalDistribution();


        if (sigG > Constants.QL_EPSILON) {
            /*@Real*/ final double x_1  = (muG-Math.log(payoff.strike())+variance)/sigG;
            Nx_1 = CND.op(x_1);
            nx_1 = ND.op(x_1);
        } else {
            Nx_1 = (muG > Math.log(payoff.strike()) ? 1.0 : 0.0);
            nx_1 = 0.0;
        }

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            final double /*@Real*/ K = -2.0*Math.log(riskFreeDiscount)/(variance*(1.0-riskFreeDiscount));
            double /*@Real*/ Q, a;
            switch (payoff.optionType()) {
            case Call:
                Q = (-(n-1.0) + Math.sqrt(((n-1.0)*(n-1.0))+4.0*K))/2.0;
                a =  (Sk/Q) * (1.0 - dividendDiscount * cumNormalDist.op(d1));
                if (spot<Sk)
                    r.value = black.value() + a * Math.pow((spot/Sk), Q);
                else
                    r.value = spot - payoff.strike();
                break;

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                else
                    r.value = spot - payoff.strike();
                break;
            case Put:
                Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0))+4.0*K))/2.0;
                a = -(Sk/Q) * (1.0 - dividendDiscount * cumNormalDist.op(-d1));
                if (spot>Sk)
                    r.value = black.value() + a * Math.pow((spot/Sk), Q);
                else
                    r.value = payoff.strike() - spot;
                break;

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        final double /*@Real*/ temp = BlackFormula.blackFormula(payoff.optionType(), payoff.strike(), forwardSi, Math.sqrt(variance))*riskFreeDiscount;
        switch (payoff.optionType()) {
        case Call:
            Q = (-(n-1.0) + Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = Si - payoff.strike();
            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;

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        switch (payoff.optionType()) {
        case Call:
            Q = (-(n-1.0) + Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = Si - payoff.strike();
            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);

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                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 = Si - payoff.strike();
                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;
        case Put:

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                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = Si - payoff.strike();
                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;
        case Put:
            Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;

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