Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

VaR

AMH
calculates the density function of a Ali-Mikhail-Haq copula.
clayton
calculates the density function of a Clayton copula.
copulalike
auxiliary quantlet for mnllike, determines the value of log-likelihood function for a given copula.
CornishFisher
Implements the Cornish-Fisher expansion for arbitrary orders. The algorithm is related, but not identical to, the algorithm "AS269" published in "Applied Statistics".
dcat
computes the conditional dependence parameter from a given copula with maximal likelihood method.
FGM
calculates the density function of a Farlie-Gumbel-Morgenstern copula.
frank
calculates the density function of a Frank copula.
galambos
calculates the density function of a Galambos copula.
gumbel
calculates the density function of a Gumbel Extreme Value Copula.
gumbelII
calculates the density function of a Gumbel II Extreme Value Copula.
mnllike
determines the maximum of the log-likelihood function for a given copula.
TailCoeffCopula
calculates the lower and upper tail-dependence coefficients for various copulae.
TailCoeffEstimation
estimates the upper or lower tail-dependence coefficient via a bivariate empirical copula.
TailCoeffEstimElliptical
estimates the upper (=lower) tail-dependence coefficient for an elliptically contoured distribution.
TailCoeffLookup
auxiliary table-search quantlet for TailCoeff* quantlets.
ucat
auxiliary quantlet for dcat.
VaRauxdiagcat
subroutine for VaRdiagtable.
VaRauxsums
subroutine for VaRpred (with option sums), calculates the transformation matrix.
VaRcdfDG
approximates the cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.
VaRcgfDG
computes the cumulant generating function (cgf) for the class of quadratic forms of Gaussian vectors.
VaRcharfDG
computes the characteristic function for the class of quadratic forms of Gaussian vectors.
VaRcharfDGF2
computes the Fourier transform of an approximating Gaussian cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.
VaRcopula
calculates the copula function, its derivatives and the inverse (in two dimensions).
VaRcorrfDGF2
computes the cumulative distribution function (CDF) of an approximated normal distribution for the class of quadratic forms of Gaussian vectors.
VaRcredN
Simulates a default distribution for a portfolio of homogeneous obligors where the default driver is normally distributed. Returns mean, variance and the quantile chosen.
VaRcredN2
Simulates a default distribution for a portfolio of obligors where the (joint) default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors. Returns mean, variance and the quantile chosen.
VaRcredTcop
Simulates a default distribution for a portfolio of homogeneous obligors where individual default drivers are normally distributed. The joint distribution is generated by the use of a t-copula. Returns mean, variance and the quantile chosen.
VaRcredTcop2
Simulates a default distribution for a portfolio of obligors where the individual default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors linked by a t-copula. Returns mean, variance and quantile chosen.
VaRcumulantDG
computes the n-th cumulant for the class of quadratic forms of Gaussian vectors.
VaRcumulantsDG
compute the first n cumulants for the class of quadratic forms of Gaussian vectors.
VaRDGdecomp
uses a generalized eigenvalue decomposition to do a suitable coordinate change. The new risk factors are independently standard normal distributed and the new Hessian matrix (Gamma) is diagonal.
VaRDGdecompG
computes the first and second derivatives with respect to the new risk factors.
VaRdiagplot
produces calibration and discrimination plots which verify the validity of a probability forecasts.
VaRdiagtable
produces table containing frequencies of predictive probabilities of the observations falling into specified intervals.
VaRest
estimates the value at risk (VaR).
VaRestMC
Partial Monte-Carlo method to calculate the Value at Risk (VaR) based on Delta-Gamma Approximation.
VaRestMCcopula
estimates VaR for a given portfolio using copulas
VaRfitcopula
fits the copula to a given data
VaRgrdiag
produces calibration and discrimination plots which verify validity of probability forecasts.
VaRmain
sets defaults for library VaR.
VaRopt
defines a list with optional parameters in VaR functions. The list is either created or new options are appended to an existing list.
VaRpred
predicts the value at risk (VaR).
VaRqDG
computes the a-quantile for the class of quadratic forms of Gaussian vectors; uses Fourier inversion to approximate the cumulative distribution function (CDF).
VaRqqplot
visualizes the reliability of VaR forecasts.
VaRRatMigCount
Derives the matrix of migration counts from the matrix of migration events
VaRRatMigRate
computes the migration rates and the related estimated standard errors from the matrix of migration counts
VaRRatMigRateM
computes the m-period transition rates. Standard deviations of the transition rates are estimated by bootstrap.
VaRsimcopula
generates 2-dimensional random data from distribution with given copula
VaRtest
VaRtest tests all quantlets of the VaR library
VaRtimeplot
shows the time plot of VaR forecasts and the associated changes of the P&L of the portfolio.
VaRver
verifies probability forecasts

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 05.02.2006Impressum