Package org.jquantlib.math.distributions

Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution

194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
        greeks.delta = futureWeight*black.delta(forwardPrice)*forwardPrice/s;
        greeks.gamma = forwardPrice*futureWeight/(s*s)*(black.gamma(forwardPrice)*futureWeight*forwardPrice
                - pastWeight*black.delta(forwardPrice) );

        /*@Real*/ double Nx_1, nx_1;
        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;
        }
View Full Code Here
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
            moreGreeks.thetaPerDay        = black.thetaPerDay(spot, t);
            moreGreeks.strikeSensitivity  = black.strikeSensitivity();
            moreGreeks.itmCashProbability = black.itmCashProbability();
        } else {
            // early exercise can be optimal
            final CumulativeNormalDistribution cumNormalDist = new CumulativeNormalDistribution();
            final double /*@Real*/ tolerance = 1e-6;
            final double /*@Real*/ Sk = criticalPrice(payoff, riskFreeDiscount, dividendDiscount, variance, tolerance);
            final double /*@Real*/ forwardSk = Sk * dividendDiscount / riskFreeDiscount;
            final double /*@Real*/ d1 = (Math.log(forwardSk/payoff.strike()) + 0.5*variance)/Math.sqrt(variance);
            final double /*@Real*/ n = 2.0*Math.log(dividendDiscount/riskFreeDiscount)/variance;
            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;
            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;
View Full Code Here
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
        // Newton Raphson algorithm for finding critical price Si
        double /*@Real*/ Q, LHS, RHS, bi;
        double /*@Real*/ forwardSi = Si * dividendDiscount / riskFreeDiscount;
        double /*@Real*/ d1 = (Math.log(forwardSi/payoff.strike()) + 0.5*variance) / Math.sqrt(variance);
        final CumulativeNormalDistribution cumNormalDist = new CumulativeNormalDistribution();
        final double /*@Real*/ K = (riskFreeDiscount!=1.0 ? -2.0*Math.log(riskFreeDiscount)/ (variance*(1.0-riskFreeDiscount)) : 0.0);
        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;
                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;
            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);
                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
61
62
63
64
65
66
67
68
         return bs.op(k);

     }

      public static  double evaluateCumulativeNormalDistribution(double mean, double sigma, double z){
          CumulativeNormalDistribution cnd = new CumulativeNormalDistribution(mean, sigma);
      return     cnd.op(z);
      }
View Full Code Here
94
95
96
97
98
99
100
101
102
103
104
    //
    // public constructors
    //

    public AnalyticBarrierEngine(final GeneralizedBlackScholesProcess process) {
        this.f = new CumulativeNormalDistribution();
        this.a = (BarrierOption.ArgumentsImpl)arguments_;
        this.r = (BarrierOption.ResultsImpl)results_;
        this.process = process;
        this.process.addObserver(this);
    }
View Full Code Here
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
        QL.info("Testing differential operators...");

        final double average = 0.0, sigma = 1.0;

        final NormalDistribution normal = new NormalDistribution(average,sigma);
        final CumulativeNormalDistribution cum = new CumulativeNormalDistribution(average,sigma);

        final double xMin = average - 4*sigma,
        xMax = average + 4*sigma;
        final int N = 10001;
        final double h = (xMax-xMin)/(N-1);

        final Array x = new Array(N), y = new Array(N), yi = new Array(N), yd = new Array(N);
        Array temp = new Array(N);
        final Array diff = new Array(N);

        int i;
        for(i=0; i < x.size(); i++) {
            x.set(i, xMin+h*i);
        }

        for(i = 0; i < x.size(); i++) {
            y.set(i, normal.op(x.get(i)));
        }

        for(i = 0; i < x.size(); i++) {
            yi.set(i, cum.op(x.get(i)));
        }

        for (i=0; i < x.size(); i++) {
            yd.set(i, normal.derivative(x.get(i)));
        }
View Full Code Here
107
108
109
110
111
112
113
114
115
116
117
118
119
120
   */
  public /*@Real*/ double gaussianRegret(/*@Real*/ double target) /* @ReadOnly */ {
        /*@Real*/ double m = this.mean();
        /*@Real*/ double std = this.standardDeviation();
        /*@Real*/ double variance = std*std;
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        /*@Real*/ double firstTerm = variance + m*m - 2.0*target*m + target*target;
        /*@Real*/ double alfa = gIntegral.op(target);
        /*@Real*/ double secondTerm = m - target;
        /*@Real*/ double beta = variance*g.op(target);
        /*@Real*/ double result = alfa*firstTerm - beta*secondTerm;
        return result/alfa;
    }
View Full Code Here
189
190
191
192
193
194
195
196
    /**
     * gaussian-assumption Shortfall (observations below target)       
     */
    public /*@Real*/ double gaussianShortfall(/*@Real*/ double target) /* @ReadOnly */ {
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(mean(), standardDeviation());
        return gIntegral.op(target);
    }
View Full Code Here
200
201
202
203
204
205
206
207
208
     * gaussian-assumption {@link Average} Shortfall (averaged shortfallness)       
     */
    public /*@Real*/ double gaussianAverageShortfall(/*@Real*/ double target) /* @ReadOnly */ {
      /*@Real*/ double m = mean();
      /*@Real*/ double std = 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) );
    }
View Full Code Here
TOP

Related Classes of org.jquantlib.math.distributions.CumulativeNormalDistribution

  • org.jquantlib.math.statistics.GaussianStatistics
  • org.jquantlib.math.statistics.GenericGaussianStatistics
  • org.jquantlib.model.shortrate.twofactormodels.G2$SwaptionPricingFunction
  • org.jquantlib.ooimpl.MathHelper
  • org.jquantlib.pricingengines.AmericanPayoffAtExpiry
  • org.jquantlib.pricingengines.AmericanPayoffAtHit
  • org.jquantlib.pricingengines.asian.AnalyticDiscreteGeometricAveragePriceAsianEngine
  • org.jquantlib.pricingengines.barrier.AnalyticBarrierEngine
  • org.jquantlib.pricingengines.BlackCalculator
  • org.jquantlib.pricingengines.BlackFormula
    • Copyright © 2018 www.massapicom. All rights reserved.
    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.