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

                lambda = Math.sqrt( mu * mu - 2.0 * Math.log(discount) / variance);
            }
            D1 = log_H_S / stdDev + lambda * stdDev;
            D2 = D1 - 2.0 * lambda * 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 {
            // TODO: not tested yet

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            }
            D1 = log_H_S / stdDev + lambda * stdDev;
            D2 = D1 - 2.0 * lambda * 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 {
            // TODO: not tested yet
            mu = Math.log(dividendDiscount / discount) / variance - 0.5;

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        @Real final double d2 = d1 - stddev;


        // TODO: code review
        final CumulativeNormalDistribution phi = new CumulativeNormalDistribution();
        @Real final double result = discount * optionType.toInteger()
        * (forward * phi.op(optionType.toInteger() * d1) - strike * phi.op(optionType.toInteger() * d2));


        if (result >= 0.0) return result;
        throw new ArithmeticException("a negative value was calculated"); // TODO: message
    }

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        @Real final double d2 = d1 - stddev;


        // TODO: code review
        final CumulativeNormalDistribution phi = new CumulativeNormalDistribution();
        @Real final double result = discount * optionType.toInteger()
        * (forward * phi.op(optionType.toInteger() * d1) - strike * phi.op(optionType.toInteger() * d2));


        if (result >= 0.0) return result;
        throw new ArithmeticException("a negative value was calculated"); // TODO: message
    }

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        final double d1 = Math.log((forward+displacement)/(strike+displacement))/stddev + 0.5*stddev;
        final double d2 = d1 - stddev;


        // TODO: code review
        final CumulativeNormalDistribution phi = new CumulativeNormalDistribution();
        return phi.op(optionType.toInteger() * d2);
    }


    // ---
    // ---
    // ---

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        if (stddev == 0.0) return discount * Math.max(d, 0.0);


        // TODO: code review
        final CumulativeNormalDistribution phi = new CumulativeNormalDistribution();
        @NonNegative
        final double result = discount * stddev * phi.derivative(h) + d * phi.op(h);
        if (result >= 0.0) return result;
        throw new ArithmeticException("negative value");
    }


    // ---

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        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) {

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        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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