Examples of com.opengamma.analytics.math.minimization.SingleRangeLimitTransform

com.opengamma.analytics.math.minimization.SingleRangeLimitTransform

If a model parameter $x$ is constrained to be either above or below some level $a$ (i.e. $x > a$ or $x < a$), the function to transform it to an unconstrained variable $y$ is given by $$ \begin{align*} y=\begin{cases} \ln(e^{x-a} - 1)\quad & x> a\\ a - \ln(e^{a-x} - 1)\quad & x < a \end{cases} \end{align*} $$ with inverse transform $$ \begin{align*} x=\begin{cases} a + \ln(e^y + 1)\quad & x> a\\ a - \ln(e^y + 1)\quad & x < a \end{cases} \end{align*} $$ For large $y> 50$, this becomes $$ \begin{align*} y=\begin{cases} x - a\quad & x > a\\ a - x\quad & x < a \end{cases} \end{align*} $$ with inverse transform $$ \begin{align*} x=\begin{cases} a + y\quad & x> a\\ a - y\quad & x < a \end{cases} \end{align*} $$ so any value of $y$ will give a value of $x$.

  }


  @Test
      (enabled = false)
      public void debugTest2() {
    final ParameterLimitsTransform trans = new SingleRangeLimitTransform(0.0, LimitType.GREATER_THAN);
    ShiftedLogNormalTailExtrapolationFitter fitter = new ShiftedLogNormalTailExtrapolationFitter();
    double f = 1.0;
    double t = 2. / 52.;
    double k = 1.1;


    double mu = 0.133333;
    double theta = 0.16;
    double p = ShiftedLogNormalTailExtrapolation.price(f, k, t, true, mu, theta);
    double dd = ShiftedLogNormalTailExtrapolation.dualDelta(f, k, t, true, mu, theta);
    System.out.println("price and DD " + p + "\t" + dd);
    System.out.println("trans " + trans.inverseTransform(-2.2521684610628063));


    double[] res = fitter.fitPriceAndGrad(f, k, p, dd, t, true);
    assertEquals("mu ", mu, res[0], 1e-8);
    assertEquals("theta", theta, res[1], 1e-8);
  }

Examples of com.opengamma.analytics.math.minimization.SingleRangeLimitTransform

Related Classes of com.opengamma.analytics.math.minimization.SingleRangeLimitTransform