Package org.jquantlib.math.distributions

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

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        final double std = statistics.standardDeviation();
        final double variance = std * std;
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        final double firstTerm = variance + m * m - 2.0 * target * m + target * target;
        final double alfa = gIntegral.op(target);
        final double secondTerm = m - target;
        final double beta = variance * g.op(target);
        final double result = alfa * firstTerm - beta * secondTerm;
        return result / alfa;
    }
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        return -Math.min(result, 0.0);
    }

    public double gaussianShortfall(final double target) {
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(statistics.mean(), statistics.standardDeviation());
        return gIntegral.op(target);
    }

    public double gaussianAverageShortfall(final double target) {
        final double m = statistics.mean();
        final double std = statistics.standardDeviation();
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    public double gaussianAverageShortfall(final double target) {
        final double m = statistics.mean();
        final double std = statistics.standardDeviation();
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        return ((target - m) + std * std * g.op(target) / gIntegral.op(target));
    }

    /*
     * TODO: where do we need this one???????????????????????????????? //! Helper class for precomputed distributions class
     * StatsHolder { public: typedef Real value_type; StatsHolder(Real mean, Real standardDeviation) : mean_(mean),
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                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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