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  pdftitle = {ghyp: A package on generalized hyperbolic distributions},
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  pdfauthor = {Marc Weibel and Henriette-Elise Breymann and David L\"uthi},
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\begin{document}
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\title{ghyp: A package on generalized hyperbolic distributions}
\author{Marc Weibel \footnote{Quantsulting GmbH, e-mail: marc.weibel@quantsulting.ch} \and Henriette-Elise Breymann\footnote{Institute of Data Analysis and
    Process Design, e-mail:
    bwlf.breymann@zhaw.ch} \and David L\"uthi\footnote{Zurich Insurance}}
%\publishers{}
\date{\today}
\maketitle
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\begin{abstract}
  In this document the generalized hyperbolic (GH) distribution is
  explained to the extent which is required to understand the
  internals of the~\R~package~\ghyp. Essentially, the density and
  moment generating functions of the GH distribution and its special
  cases are provided together with some important properties of the GH
  family. In addition the algorithm underlying the fitting procedure
  for the multivariate GH distribution is explained. Then we shed
  light on how to switch between different parametrizations of the GH
  distribution. In the appendix we describe the generalized inverse
  Gaussian distribution and give some useful facts regarding the
  modified Bessel function of the third kind. Finally, we write on the
  design on the package~\ghyp~and give some code chunks which indicate
  how the software can be used.
\end{abstract}
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\thispagestyle{empty}
\clearpage
\pagenumbering{arabic}
\tableofcontents{}
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% <---------------------------------------------------------------------->
\section{Introduction}
% <---------------------------------------------------------------------->
The origin of this package goes back to the first authors' years at
RiskLab, when he worked together with Alexander McNeil to develop an
algorithm for fitting multivariate generalized hyperbolic (GH)
distributions. Accordingly, the functionality of this package partly
overlaps McNeil's library QRMlib \citep{McNeil2005a}.  However, there
are quite some differences in the implementation.  From the user's
point of view, an important difference may be that one can choose
between different parametrizations. In addition, with the present
library it is possible to fit multivariate as well as univariate
generalized hypberbolic distributions in a unified
framework. Furthermore, the package~\ghyp~provides a much richer set
of functionality, including many plotting resources and graphical
diagnostic tools as well as various utilities as the likelihood-ratio
test, Akaike information based model selection, and linear
transformations for instance. In general, we put emphasis on financial
application and provide several risk and performance measurement
functionalities and a portfolio optimization routine. The finance
related facilities are not described in this document, but in the
reference manual of the package~\ghyp.  This document is primarily
intended to give the necessary information and formulas to deal with
univariate and multivariate generalized hyperbolic distributions. It
focuses on (i) different parametrizations, (ii) fitting multivariate
GH distributions, and (iii) special cases of the GH distribution.

Please note that this document is by far not complete. A nice
reference for univariate GH distributions and the generalized inverse
Gaussian (GIG) distribution is \citet*{Prause1999} and
\citet*{Paolella2007}. An instructive guide for multivariate GH
distributions is \citet*{McNeil2005}, where one can find also some
empirical work and applications of the GH distribution in finance.
% <---------------------------------------------------------------------->
\section{Definition}
% <---------------------------------------------------------------------->
Facts about generalized hyperbolic (GH) distributions are cited
according to \citet*{McNeil2005} chapter $3.2$.\\[1ex]
% <---------------------------------------------------------------------->
The random vector $\X$ is said to have a multivariate GH distribution
if
\begin{equation}\label{eq:ghd}
  \X \stackrel{\text{d}}{=} \MU + W \GAMMA + \sqrt{W} A \Z
\end{equation}
where
\begin{enumerate}
\item $\Z \sim N_k(\mathbf{0},I_k)$
\item $A \inReal^{d \times k}$
\item $ \MU, \GAMMA \inReal^{d}$
\item $W \geq 0$ is a scalar-valued random variable which is
  independent of $\Z$ and has a Generalized Inverse Gaussian
  distribution, written $GIG(\lambda, \chi, \psi)$ (cf. appendix
  \ref{sec:gig}).
\end{enumerate}
Note that there are at least five alternative definitions leading to
different parametrizations. In section \ref{sec:param} we will present
the parametrizations which are implemented in the package~\ghyp. The
above definition, though intuitive, acquires an identifiability
problem which will be described in section \ref{sec:chi-psi-param}.
The parameters of a GH distribution given by the above definition
admit the following interpretation:
\begin{itemize}
\item $\lambda, \chi, \psi$ determine the shape of the distribution.
  That is, how much weight is assigned to the tails and to the
  center. In general, the larger those parameters the closer the
  distribution is to the normal distribution.
\item $\MU$ is the location parameter.
\item $\Sigma = A A'$ is the dispersion-matrix.
\item $\GAMMA$ is the skewness parameter. If $\GAMMA = 0$, then the
  distribution is symmetric around $\MU$.
\end{itemize}
Observe that the conditional distribution of $\X | W = w$ is normal,
\begin{equation}\label{eq:mixture}
  \X | W = w \sim \Gauss_d(\MU + w \, \GAMMA, w \Sigma),
\end{equation}
where $\Sigma = A A'$.
% <---------------------------------------------------------------------->
\subsection{Expected value and variance}
% <---------------------------------------------------------------------->
The expected value and the variance are given by
\begin{eqnarray}
  \EXP{\X}  &=& \MU + \EXP{W} \GAMMA \\
  \VAR{\X}  &=& \EXP{\COV{\X | W}} + \COV{\EXP{\X | W}} \\ \nonumber
  &=& \VAR{W} \GAMMA \GAMMA'  + \EXP{W} \Sigma.
\end{eqnarray}
% <---------------------------------------------------------------------->
\subsection{Density}
% <---------------------------------------------------------------------->
Since the conditional distribution of $\X$ given $W$ is Gaussian with
mean $\MU + W \GAMMA$ and variance $ W \Sigma$ the GH density can be
found by mixing $\X | W$ with respect to $W$.
\begin{eqnarray}\label{eq:fghyp}
  f_\X(\x) &=& \int_0^\infty f_{\X|W}(\x|w) \, f_W(w) \, \dd w \\ \nonumber
  &=& \int_0^\infty
  \frac{e^{(\x-\MU)'\InvSigma\GAMMA}}
  {(2\pi)^{\frac{d}{2}}\dete{\Sigma}^{\OneDivTwo}w^{\dDivTwo}}
  \exp\curlybrackets{-\frac{\QX}{2w}-\frac{\GammaSigGamma}{2/w}}
  f_W(w)dw \\ \nonumber
  &=& \frac{(\sqrt{\psi/\chi})^\lambda
    (\psi + \GammaSigGamma)^{\dDivTwo-\lambda}}
  {(2\pi)^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
    \BesselLambda(\SqrtChiPsi)}\times
  \frac{\Bessel{\lambda - \dDivTwo}(
    \sqrt{(\chi + \QX)(\psi + \GammaSigGamma)})\;
    e^{(\x - \MU)'\InvSigma \GAMMA}}
  {(\sqrt{(\chi + \QX)(\psi + \GammaSigGamma)})^{\dDivTwo - \lambda}},
\end{eqnarray}
where the relation (\ref{eq:besselrelation}) for the modified Bessel
function of the third kind $\BesselLambda(\cdot)$ is used (cf. section
\ref{sec:bessel}) and $\QX$ denotes the mahalanobis distance $\QX =
(\x - \MU)' \InvSigma (\x - \MU)$.  The domain of variation of the
parameters $\lambda, \chi$ and $\psi$ is given in section
\ref{sec:param} and appendix \ref{sec:gig}.
% <---------------------------------------------------------------------->
\subsection{Moment generating function}
% <---------------------------------------------------------------------->
An appealing property of normal mixtures is that the moment generating
function is easily calculated once the moment generating function of
the mixture is known. Based on equation (\ref{eq:moment-gen-gig}) we
obtain the moment generating function of a GH distributed random
variable $X$ as
\begin{eqnarray}
  \label{eq:1}
    \momgen_{GH}(t) &=& \EXP{\EXP{\exp\curlybrackets{\tvec' \X} | W}} =
    e^{\tvec'\MU} \EXP{\exp\curlybrackets{W \parentheses{\tvec' \GAMMA + 1/2~\tvec'
          \Sigma \tvec}}} \nonumber \\
    &=& e^{\tvec' \MU} \parentheses{\frac{\psi}{\psi - 2 \tvec'
        \GAMMA - \tvec' \Sigma \tvec}}^{\lambda / 2}
    \frac{\BesselLambda(\sqrt{\psi (\chi - 2 \tvec'
        \GAMMA - \tvec' \Sigma \tvec)})}{\BesselLambda(\sqrt{\chi
        \psi})}, \quad \chi \geq 2~\tvec' \gamma + \tvec' \Sigma
    \tvec. \nonumber
\end{eqnarray}
For moment generating functions of the special cases of the GH
distribution we refer to \citet*{Prause1999} and \citet*{Paolella2007}.
% <---------------------------------------------------------------------->
\subsection{Linear transformations}\label{sec:lin-transf}
% <---------------------------------------------------------------------->
The GH class is closed under linear transformations:
\begin{proposition}
  If $\X \sim \GH$ and $\Y = B \X + \mathbf{b}$, where $B \in
  \Real^{k \times d}$ and $\mathbf{b} \in \Real^k$, then $Y
  \sim \GHtransf$.
\end{proposition}
\begin{proof}
  The characteristic function of $\X$ is
  $$\phi_X(\tvec) = \EXP{\EXP{e^{i \, \tvec' \X} | W}} =
  \EXP{e^{i \, \tvec' (\MU + W \GAMMA) - 1/2 \, W \, \tvec' \, \Sigma
      \, \tvec}} = e^{i \, \tvec' \MU} \, \hat{H}(1/2 \, \tvec' \,
  \Sigma \, \tvec - i \, \tvec' \GAMMA),$$ where $\hat{H}(\theta) =
  \int_0^\infty e^{-\theta v} dH(v)$ denotes the Laplace-Stieltjes
  transform of the distribution function $H$ of $W$. Let $\Y = B \, \X
  + \mathbf{b}$. The characteristic function is then
  \begin{eqnarray*}
    \phi_Y(t) &=& \EXP{\EXP{e^{i \, \tvec' \, (B \, \X  + \mathbf{b})} | W}} =
    \EXP{e^{i \, \tvec' \, (B (\MU + W \GAMMA) + \mathbf{b}) -
        1/2 \, W \,\tvec' \, B \, \Sigma \, B' \, \tvec}}\\
    &=& e^{i \, \tvec' \, (B \, \MU + \mathbf{b})} \, \hat{H}(1/2
    \, \tvec' \, B \, \Sigma \, B' \, \tvec -  i \, \tvec' B \, \GAMMA).
  \end{eqnarray*}
  Therefore, $B \X + \mathbf{b} \sim \GHtransf$.
\end{proof}
% <---------------------------------------------------------------------->
\section{Special cases of the generalized hyperbolic
  distribution}\label{sec:specialcases}
% <---------------------------------------------------------------------->
The GH distribution contains several special cases known under special
names.
\begin{itemize}
\item{If $\lambda = \frac{d+1}{2}$ the name generalized is dropped and
    we have a multivariate \emph{hyperbolic (hyp)} distribution. The
    univariate margins are still GH distributed. Inversely, when
    $\lambda = 1$ we get a multivariate GH distribution with
    hyperbolic margins.}
\item{If $\lambda = -\frac{1}{2}$ the distribution is called
    \emph{Normal Inverse Gaussian (NIG)}.}
\item{If $\chi = 0$ and $\lambda > 0$ one gets a limiting case which
    is known amongst others as \emph{Variance Gamma (VG)}
    distribution.}
\item{If $\psi = 0$ and $\lambda < 0$ the \emph{generalized
      hyperbolic Student-t} distribution is obtained (called simply
    \emph{Student-t} in what follows).}
\end{itemize}
Further information about the special cases and the necessary formulas
to fit these distributions to multivariate data can be found in the
appendixes \ref{sec:gig} and \ref{sec:dens_special_cases}. The
parameter constraints for the special cases in different
parametrizations are described in the following section.
% <---------------------------------------------------------------------->
\section{Parametrization}\label{sec:param}
% <---------------------------------------------------------------------->
There are several alternative parametrizations for the GH
distribution.  In the~\R~package~\ghyp~the user can choose between
three of them.  There exist further parametrizations which are not
implemented and not mentioned here. For these parametrizations we
refer to \citet*{Prause1999} and \citet*{Paolella2007}.

Appendix \ref{sec:exampledistobj} explains how to use the
parametrizations implemented in the package~\ghyp.  Table
\ref{tab:param} describes the parameter ranges for each
parametrization and each special case. Clearly, the dispersion
matrices $\Sigma$ and $\Delta$ have to fulfill the usual conditions
for covariance matrices, i.e., symmetry and positive definiteness as
well as full rank. Appendix \ref{sec:exampledistobj} also gives a
table where the constructor functions for each combination of
distribution and parametrization are listed.

\begin{table}[h]
  \begin{small}
    \begin{tabular}{l | c c c c c c}
      & \multicolumn{6}{c}{$(\LambdaChiPsi)$-Parametrization} \\
      &  $\lambda$ & $\chi$ & $\psi$ & $\MU$ & $\Sigma$ & $\GAMMA$\\
      \hline
      ghyp & $\lambda \inReal$ & $\chi > 0$ & $\psi > 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      hyp & $\lambda = \frac{d + 1}{2} $ & $\chi > 0$ & $\psi > 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      NIG & $\lambda = - \OneDivTwo$ & $\chi > 0$ & $\psi > 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      t & $\lambda < 0$ & $\chi > 0$ & $\psi = 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      VG & $\lambda > 0$ & $\chi = 0$ & $\psi > 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      \hline
      \\
      % -------------------------------------------------------------------------
      & \multicolumn{6}{c}{$(\LambdaAbar)$-Parametrization} \\
      &  $\lambda$ & $\abar$ & $\MU$ & $\Sigma$ & $\GAMMA$\\
      \hline
      ghyp & $\lambda \inReal$ & $\abar > 0$ & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      hyp & $\lambda = \frac{d + 1}{2} $ & $\abar > 0$  & $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      NIG & $\lambda = \OneDivTwo$ & $\abar > 0$ &  $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      t & $\lambda = - \frac{\nu}{2} < -1$ & $\abar = 0$ &   $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      VG & $\lambda > 0$ & $\abar = 0$ &  $\MU \inReal^d$ & $\Sigma \inRealSigma$ & $\GAMMA \inReal^d$\\
      \hline
      \\
      % -------------------------------------------------------------------------
      & \multicolumn{6}{c}{$(\BarndorffParam)$-Parametrization} \\
      &  $\lambda$ & $\alpha$ & $\delta$ & $\MU$ & $\Delta$ & $\BETA$\\
      \hline
      ghyp & $\lambda \inReal$ & $\alpha > 0$ & $\delta > 0$ & $\MU \inReal^d$ & $\Delta \inRealDelta$ & $\BETA \in \{\x \inReal^d : \alpha^2 - \x' \Delta \x > 0\}$ \\
      hyp & $\lambda = \frac{d + 1}{2} $ & $\alpha > 0$ & $\delta > 0$ & $\MU \inReal^d$ & $\Delta \inRealDelta$  & $\BETA \in \{\x \inReal^d : \alpha^2 - \x' \Delta \x > 0\}$ \\
      NIG & $\lambda = - \OneDivTwo$ & $\alpha > 0$ & $\delta > 0$ & $\MU \inReal^d$ & $\Delta \inRealDelta$  & $\BETA \in \{\x \inReal^d : \alpha^2 - \x' \Delta \x > 0\}$\\
      t & $\lambda < 0$ & $\alpha = \sqrt{\BETA' \Delta \BETA}$ & $\delta > 0$ & $\MU \inReal^d$ & $\Delta \inRealDelta$  & $\BETA \inReal^d$ \\
      VG & $\lambda > 0$ & $\alpha > 0$ & $\delta = 0$ & $\MU \inReal^d$ & $\Delta \inRealDelta$  & $\BETA \in \{\x \inReal^d : \alpha^2 - \x' \Delta \x > 0\}$\\
      \hline
    \end{tabular}
  \end{small}
  \caption{The domain of variation for the parameters of the GH distribution and
    some of its special cases for different parametrizations. We denote the set of all feasible covariance
    matrices in $\Real^{d \times d}$ with $\Real^\Sigma$.
    Furthermore, let $\Real^\Delta = \{A \inRealSigma : \dete{A} = 1\}$.}\label{tab:param}
\end{table}
% <---------------------------------------------------------------------->
\subsection{$(\LambdaChiPsi)$-Parametrization}\label{sec:chi-psi-param}
% <---------------------------------------------------------------------->
The ($\LambdaChiPsi$)-parametrization is obtained as the normal
mean-variance mixture distribution when $W \sim GIG(\lambda, \chi,
\psi)$.  This parametrization has a drawback of an identification
problem. Indeed, the distributions $\GH$ and $\GHk$ are identical for
any $k > 0$. Therefore, an identifying problem occurs when we start to
fit the parameters of a GH distribution to data. This problem may be
solved by introducing a suitable contraint. One possibility is to
require the determinant of the dispersion matrix $\Sigma$ to be $1$.
% <---------------------------------------------------------------------->
\subsection{($\LambdaAbar$)-Parametrization}\label{sec:lambdaabarparam}
% <---------------------------------------------------------------------->
There is a more elegant way to eliminate the degree of freedom than
constraining the determinant of the dispersion matrix $\Sigma$ to
$1$. We simply require the expected value of the generalized inverse
Gaussian distributed mixing variable $W$ to be $1$
(cf. \ref{sec:gig}). This makes the interpretation of the skewness
parameters $\gamma$ easier and in addition, the fitting procedure
becomes faster (cf. \ref{sec:EM}).

Note that the expected value of a GIG distributed random variable
exists as long as $\psi > 0$. If $\psi \rightarrow 0$ the GIG
distribution approaches the Inverse Gamma distribution (the mixing
distribution belonging to a Student-t distribution) for which the
expectation only exists if $\gamma < -1$ (cf. appendix \ref{sec:gig}
and \ref{sec:studentt}).

We define
\begin{equation}
  \EXP{W}  =  \sqrt{\frac{\chi}{\psi}}\frac{\BesselLambdaPlusOne(\SqrtChiPsi)}
  {\BesselLambda{(\SqrtChiPsi)}} = 1.
\end{equation}
and set
\begin{equation}
  \abar = \SqrtChiPsi.
\end{equation}
It follows that
\begin{equation} \label{eq:abartochipsi} \psi = \abar \;
  \frac{\BesselLambdaPlusOne(\abar)}{\BesselLambda (\abar)} \;
  \hbox{and} \; \chi = \frac{\abar^2}{\psi}= \abar \;
  \frac{\BesselLambda(\abar)}{\BesselLambdaPlusOne (\abar)}.
\end{equation}
Note that whenever $\lambda = -0.5$ (NIG distribution) we have that
$\psi = \abar$ and $\chi = \abar$. This is because of the symmetry of
the Bessel function (cf. equation \ref{eq:bessel-symmetry}).

The drawback of the ($\LambdaAbar$)-parametrization is that it does
not exist in the case $\abar = 0$ and $\lambda \in [-1,0]$, which
corresponds to a Student-t distribution with non-existing
variance. Note that the ($\LambdaAbar$)-parametrization yields to a
slightly different parametrization for the special case of a Student-t
distribution (cf. section \ref{sec:studentt} for details). The limit
of the equations (\ref{eq:abartochipsi}) as $\abar \downarrow 0$ can
be found in (\ref{eq:abartochipsiinvgamma}) and
(\ref{eq:abartochipsigamma}).
% <---------------------------------------------------------------------->
\subsection{$(\BarndorffParam)$-Parametrization}
% <---------------------------------------------------------------------->
When the GH distribution was introduced in \citet{Barndorff-Nielsen1977a},
the following parametrization for the multivariate case was used:
\begin{equation}
  f_\X(\x)  = \frac{ (\KAPPA)^{\lambda /2}}{(2\pi)^{\dDivTwo}
    \sqrt{\dete{\Delta}} \, \alpha^{\lambda - \dDivTwo} \delta^\lambda \,
    \BesselLambda(\delta \sqrt{\KAPPA})}\times
  \frac{\Bessel{\lambda - \dDivTwo}(\alpha \sqrt{\delta^2 + \Qdelta}) \;
    e^{\BETA' (\x - \MU)}}{(\sqrt{\delta^2 + \Qdelta})^{\dDivTwo - \lambda}}.
\end{equation}
Similar to the ($\LambdaChiPsi$) parametrization, there is an
identifying problem which can be solved by constraining the
determinant of $\Delta$ to 1.
The mixture representation belonging to this parametrization is
\begin{eqnarray}
  \label{eq:mixture-alpha-delta-param}
  X | W = w &\sim& \Gauss_d(\MU + w \BETA \Delta, w \Delta)\\
  W &\sim& GIG(\lambda, \delta^2, \alpha^2 - \BETA' \Delta \BETA).
\end{eqnarray}
In the univariate case the above expression reduces to
\begin{equation}
  f_\X(\x)  = \frac{ (\alpha^2 - \beta^2)^{\lambda /2}}
  {\sqrt{2\pi} \, \alpha^{\lambda - \OneDivTwo} \, \delta^\lambda \,
    \BesselLambda(\delta \sqrt{\alpha^2 - \beta^2})}\times
  \frac{\Bessel{\lambda - \OneDivTwo}(\alpha \sqrt{\delta^2 + (x -
      \mu)^2})}{(\sqrt{\delta^2 + (x - \mu)^2})^{\OneDivTwo - \lambda}} \;
  e^{\beta (x - \mu)},
\end{equation}
which is the most widely used parametrization of the GH distribution
in literature.
% <---------------------------------------------------------------------->
\subsection{Switching between different parametrizations}
% <---------------------------------------------------------------------->
The following formulas can be used to switch between the
($\LambdaAbar$), ($\LambdaChiPsi$), and the
($\BarndorffParam$)-parametrization. The parameters $\lambda$
and $\MU$ remain the same, regardless of the parametrization.

The way to obtain the ($\BarndorffParam$)-parametrization from the
($\LambdaAbar$)-parametrization leads over the
($\LambdaChiPsi$)-parametrization:
$$(\LambdaAbar) \quad \leftrightarrows \quad (\LambdaChiPsi)
\quad \leftrightarrows \quad (\BarndorffParam)$$
\begin{description}
\item[$(\LambdaAbar) \rightarrow (\LambdaChiPsi)$:] Use the relations
  in (\ref{eq:abartochipsi}) to obtain $\chi$ and $\psi$.  The
  parameters $\Sigma$ and $\GAMMA$ remain the same.
\item[$(\LambdaChiPsi) \rightarrow (\LambdaAbar)$:] Set $k =
  \sqrt{\frac{\chi}{\psi}}\frac{\BesselLambdaPlusOne(\SqrtChiPsi)}
  {\BesselLambda{(\SqrtChiPsi)}}.$
  \begin{equation}
    \abar = \SqrtChiPsi , \quad \Sigma \equiv k \, \Sigma, \quad \GAMMA \equiv k \, \GAMMA
  \end{equation}
\item[$(\LambdaChiPsi) \rightarrow (\BarndorffParam)$:]
  \begin{eqnarray}
    \Delta = \dete{\Sigma}^{-\frac{1}{d}} \Sigma &,& \BETA = \Sigma^{-1} \GAMMA \nonumber \\
    \delta = \sqrt{\chi \dete{\Sigma}^{\frac{1}{d}}} &,& \alpha =
    \sqrt{\dete{\Sigma}^{-\frac{1}{d}} (\psi + \GammaSigGamma)}
  \end{eqnarray}
\item[$(\BarndorffParam) \rightarrow (\LambdaChiPsi)$:]
  \begin{equation}
    \Sigma = \Delta , \quad \GAMMA = \Delta \BETA , \quad \chi =
    \delta^2 ,
    \quad \psi = \alpha^2 - \BETA' \Delta \BETA .
  \end{equation}
\end{description}
% <---------------------------------------------------------------------->
\subsection{Location and scale invariant parametrizations}
% <---------------------------------------------------------------------->
All parametrizations mentioned above can be modified to location and
scale invariant parametrizations. For a $d$-variate GH random variable
$\X$ there may occur problems if it is linearly transformed ($\Y = B
\X + \mathbf{b}$) while $B$ affects the dimensionality (i.e.  $B
\notin \Real^{d \times d}$).  Thus, let's consider the univariate case
for a moment.

\ref{sec:lin-transf} Let $\sigma^2 = \Sigma$. For the
($\LambdaChiPsi$) and ($\LambdaAbar$) parametrization one can define
$\bar{\gamma} = \sigma \gamma$. The density reads
\begin{eqnarray}\label{eq:fghyp-loc-scale}
  f_X(x) &=& \frac{(\sqrt{\psi/\chi})^\lambda
    (\psi + \gbar^2)^{\OneDivTwo-\lambda}}
  {\sqrt{2\pi} \sigma \BesselLambda(\SqrtChiPsi)}\times
  \frac{\Bessel{\lambda - \OneDivTwo}(
    \sqrt{(\chi + q(x)^2)(\psi + \gbar^2)})\;
    e^{q(x) \gbar}}
  {(\sqrt{(\chi + q(x)^2)(\psi + \gbar^2)})^{\OneDivTwo - \lambda}},
\end{eqnarray}
where $q(x) = (x - \mu)/\sigma$.

In case of the ($\BarndorffParam$) parametrization one can define
$\abar = \alpha \delta$ and $\bbar = \beta \delta$ (see the appendix
of \citet*{Prause1999}) to get the invariant density
\begin{equation}
  f_X(x)  = \frac{(\abar^2 - \bbar^2)^{\lambda /2}}
  {\sqrt{2\pi} \, \abar^{\lambda - \OneDivTwo} \, \delta \,
    \BesselLambda(\sqrt{\abar^2 - \bbar^2})}\times
  \frac{\Bessel{\lambda - \OneDivTwo}(\abar \sqrt{1 +
      q(x)^2})}{(\sqrt{1 + q(x)^2})^{\OneDivTwo - \lambda}} \;
  e^{\beta q(x)}.
\end{equation}
% Observe that by introducing a new skewness parameter $\bar{\GAMMA} =
% \Sigma \GAMMA$, all the shape and skewness parameters ($\lambda, \chi,
% \psi, \bar{\GAMMA}$) become location and scale-invariant, provided the
% transformation does not affect the dimensionality (i.e.
% $B \inReal^{d \times d}$ and $\mathbf{b} \inReal^d$).
% <---------------------------------------------------------------------->
\subsection{Numerical implementation}\label{sec:param-implementation}
% <---------------------------------------------------------------------->
Internally, he package \ghyp~uses the
$(\LambdaChiPsi)$-parametrization. However, fitting is done in the
($\LambdaAbar$)-parametrization since this parametrization does not
necessitate additional constraints to eliminate the redundant degree
of freedom.  Consequently, what cannot be represented by the
($\LambdaAbar$)-parametrization cannot be fitted (cf. section
\ref{sec:lambdaabarparam}).
% <---------------------------------------------------------------------->
\section{Fitting generalized hyperbolic distributions to data}
% <---------------------------------------------------------------------->
Numerical optimizers can be used to fit univariate GH
distributions to data by means of maximum likelihood estimation.
Multivariate GH distributions
can be fitted with expectation-maximazion (EM) type algorithms (see
\citet{Dempster1977} and \citet{Meng1993}).
%-------------------------------------------------------------------------
\subsection{EM-Scheme}\label{sec:EM}
% <---------------------------------------------------------------------->
Assume we have iid data $\x_1,\ldots,\x_n$ and parameters represented
by $\Theta = (\LambdaAbar)$. The problem is to maximize
\begin{equation}
  \ln L(\Theta;\x_1,\ldots,\x_n) = \sum_{i=1}^n \ln f_\X(\x_i;\Theta).
\end{equation}
This problem is not easy to solve due to the number of parameters and
necessity of maximizing over covariance matrices.  We can proceed by
introducing an augmented likelihood function
\begin{equation}\label{eq:likelihood}
  \ln \tilde{L}(\Theta;\x_1,\ldots,\x_n,w_1,\ldots,w_n) =
  \sum_{i=1}^n \ln f_{\X|W}(\x_i|w_i;\MuSigGamma) +
  \sum_{i=1}^n \ln f_W(w_i;\lambda,\abar)
\end{equation}
and spend the effort on the estimation of the latent mixing variables
$w_i$ coming from the mixture representation (\ref{eq:mixture}).  This
is where the EM algorithm comes into play.
\begin{description}
\item[\textbf{E-step:}]{Calculate the conditional expectation of the
    likelihood function (\ref{eq:likelihood}) given the data
    $\x_1,\ldots,\x_n$ and the current estimates of parameters
    $\ThetaK$}.  This results in the objective function
  \begin{equation}
    Q(\Theta;\ThetaK)=\EXP{
      \ln \tilde{L}(\Theta;\x_1,\ldots,\x_n,w_1,\ldots,w_n)|
      \x_1,\ldots,\x_n;\ThetaK}.
  \end{equation}

\item[\textbf{M-step:}]{Maximize the objective function with respect
    to $\Theta$ to obtain the next set of estimates $\Theta^{[k+1]}$.}
\end{description}
Alternating between these steps yields to the maximum likelihood
estimation of the parameter set $\Theta$. \customspace In practice,
performing the E-Step means maximizing the second summand of
(\ref{eq:likelihood}) numerically. The log density of the GIG
distribution (cf. \ref{eq:densgig}) is
\begin{equation}\label{eq:logfgig}
  \ln f_W(w) = \LambdaDivTwo \ln(\psi/\chi) - \ln(2 \BesselLambda \SqrtChiPsi)
  + (\lambda - 1) \ln w -\ChiDivTwo \frac{1}{w} - \PsiDivTwo w.
\end{equation}
When using the ($\lambda,\abar$)-parametrization this problem is of
dimension two instead of three as it is in the ($\lambda, \chi,
\psi$)-parametrization.
As a consequence the performance increases.

Since the $w_i$'s are latent one has to replace $w$, $1/w$ and $\ln w$
with the respective expected values in order to maximize the log
likelihood function.  Let
\begin{equation} \label{eq:etadeltaxi} \etaIK := \EXP{w_i \, | \,
    \x_i;\ThetaK},\; \deltaIK := \EXP{w_i^{-1} \, | \,
    \x_i;\ThetaK},\; \xiIK := \EXP{\ln w_i \, | \, \x_i;\ThetaK}.
\end{equation}
We have to find the conditional density of $w_i$ given $\x_i$ to
calculate these quantities (cf. (\ref{eq:conddistGIG})).
% <---------------------------------------------------------------------->
\subsection{MCECM estimation}
% <---------------------------------------------------------------------->
In the \texttt{R} implementation a modified EM scheme is used, which
is called multi-cycle, expectation, conditional estimation (MCECM)
algorithm \citep*{Meng1993, McNeil2005}.  The different steps of the
MCECM algorithm are sketched as follows:
\renewcommand{\labelenumi}{(\arabic{enumi})}
\begin{enumerate}
\item{Select reasonable starting values for $\ThetaK$.  For example
    $\lambda = 1$, $\abar= 1$, $\MU$ is set to the sample mean,
    $\Sigma$ to the sample covariance matrix and $\GAMMA$ to a zero
    skewness vector.  }
\item{Calculate $\chi^{[k]}$ and $\psi^{[k]}$ as a function of
    $\abar^{[k]}$ using (\ref{eq:abartochipsi}).  }
\item{Use (\ref{eq:etadeltaxi}), (\ref{eq:momentgig}) and
    (\ref{eq:conddistGIG}) to calculate the weights $\etaIK$ and
    $\deltaIK$. Average the weights to get
    \begin{equation}
      \bar{\eta}^{[k]} = \OneDivN \sumN \etaIK
      \; \hbox{and} \;
      \bar{\delta}^{[k]} = \OneDivN \sumN \deltaIK.
    \end{equation}
  }
\item{If an asymmetric model is to be fitted set $\GAMMA$ to
    $\mathbf{0}$, else set
    \begin{equation}
      \GAMMA^{[k+1]}= \OneDivN \frac{\sumN \deltaIK (\bar{\x}-\x_i)}
      {\bar{\eta}^{[k]} \bar{\delta}^{[k]} - 1}.
    \end{equation}
  }
\item{Update $\MU$ and $\Sigma$:
    \begin{eqnarray}
      \MU^{[k+1]} &=& \OneDivN \frac{\sumN \deltaIK \x_i-\GAMMA^{[k+1]}}
      {\bar{\delta}^{[k]}}\\
      \Sigma^{[k+1]} &=& \OneDivN \sumN \deltaIK (\x_i-\MU^{[k+1]})(\x_i-\MU^{[k+1]})'
      - \bar{\eta}^{[k]} \GAMMA^{[k+1]} \GAMMA^{[k+1]}\,'.
    \end{eqnarray}
  }
\item{Set $\Theta^{[k,2]} = (\lambda^{[k]}, \abar^{[k]},\MU^{[k+1]},
    \Sigma^{[k+1]}, \GAMMA^{[k+1]})$ and calculate weights
    $\eta_i^{[k,2]}$, $\delta_i^{[k,2]}$ and $\xi_i^{[k,2]}$ using
    (\ref{eq:etadeltaxi}), (\ref{eq:eloggig}) and
    (\ref{eq:momentgig}).  }
\item{Maximize the second summand of (\ref{eq:likelihood}) with
    density (\ref{eq:logfgig}) with respect to $\lambda$, $\chi$ and
    $\psi$ to complete the calculation of $\Theta^{[k,2]}$ and go back
    to step (2). Note that the objective function must calculate
    $\chi$ and $\psi$ in dependence of $\lambda$ and $\abar$ using
    relation (\ref{eq:abartochipsi}).  }
\end{enumerate}
% <---------------------------------------------------------------------->
\section{Applications, comments and references}
\label{sec:applications}
% <---------------------------------------------------------------------->
Even though the GH distribution was initially ivented to study the
distribution of the logarithm of particle sizes, we will focus on
applications of the GH distribution family in finance and risk
measurement.

We have seen above that the GH distribution is very flexible in the
sense that it nests several other distributions such as the Student-t
(cf. \ref{sec:studentt}). To give some references and applications of
the GH distribution let us first summarize some of its important
properties. Beside of the above mentioned flexibility, three major
facts led to the popularity of GH distribution family in finance:
\begin{enumerate}
\item The GH distribution features both \emph{fat tails} and
  \emph{skewness}. These properties account for some of the frequently
  reported stylized facts of financial returns but also of financial
  return volatility (hyperbolic GARCH effects).
\item The GH family is naturally extended to multivariate
  distributions\footnote{The extension to multivariate distributions
    is natural because of the mixing structure (see
    eq. (\ref{eq:mixture})).}. A multivariate GH distribution does
  exhibit some kind of \emph{non-linear dependence}, for example
  \emph{tail-dependence}. This reflects the fact that extremes mostly
  occur for several risk-drivers simultaneously in financial
  markets. This property is of fundamental importance for
  risk-management, and can influence for instance portfolio weights.
\item The GH distribution is infinitely divisible
  (cf. \cite{Barndorff-Nielsen1977}). This is a necessary and sufficient
  condition to build \emph{L\'evy processes}. L\'evy processes are
  widespread in finance because of their time-continuity and their
  ability to model jumps.
\end{enumerate}

Based on these properties one can classify the applications of the GH
distributions into the fields \emph{empirical modelling}, \emph{riks
  and dependence modelling}, \emph{L\'evy processes  \& derivatives}, and
\emph{portfolio selection}.  In the following, we try to assign papers
to each of the classes of applications mentioned above. Rather than
giving summaries and conclusions for each paper, we simply cite them
and refer the interested reader to the articles and the references
therein. Note that some articles deal with special cases of the GH
distribution only.
\begin{description}
\item[Empirical modelling:]
  \citet{Eberlein1995,Barndorff-Nielsen2001,Forsberg2002,Davidson2004,Fergusson2006}.
\item[Risk and dependence modelling:]
  \citet{Eberlein1998,Breymann2003,McNeil2005,Chen2005,Kassberger2006}.
\item[L\'evy processes \& derivatives:]
  \citet{Barndorff-Nielsen1997,Barndorff-Nielsen1997a,Bibby1997,Madan1998,Raible2000,Cont2003,Prause1999}.
\item[Portfolio selection:]
  \citet{Kassberger2007}.
\end{description}
\clearpage
\begin{appendix}
% <---------------------------------------------------------------------->
\section{Shape of the univariate generalized hyperbolic distribution}
% <---------------------------------------------------------------------->
\begin{figure}[!h]
\begin{center}
\setkeys{Gin}{width=0.75\textwidth}
<<echo=FALSE>>=
  library(ghyp)
@
<<fig=TRUE,echo=FALSE>>=
  lambda <- -2:2
  a.bar <- 0.5 * 0:3 +0.01

  x.range <- 0.45
  y.range <- 0.4
  x.seq <- seq(-4, 4, length = 101)
  x.seq.range <- x.range / diff(range(x.seq)) * x.seq

  par(mfrow=c(2,2), omi=0.5*c(1.7,0.8,0.8,0),mai=c(0,0,0,0))

  plot(0,0,type="n",ylim=range(lambda)+c(-0.5,1),xlim=c(-0.3,max(a.bar)+0.5),
       xlab=expression(bar(alpha)),ylab=expression(lambda),xaxt="n")
  for(i in 1:length(a.bar)){
    for(j in 1:length(lambda)){
       gh.obj <- ghyp(alpha.bar=a.bar[i],lambda=lambda[j],gamma=0)
       gh.dens <- dghyp(x.seq,gh.obj)
       gh.dens <- y.range/diff(range(gh.dens))*gh.dens

       lines(x.seq.range+a.bar[i],gh.dens+lambda[j])
       points(a.bar[i],lambda[j])
    }
  }
  legend("topleft",legend=expression(paste("Symmetric: ",gamma," = 0")),bty="n",lty="blank")

  plot(0,0,type="n",ylim=range(lambda)+c(-0.5,1),xlim=c(-0.3,max(a.bar)+0.5),
       xlab=expression(bar(alpha)),yaxt="n",ylab="",xaxt="n")
  for(i in 1:length(a.bar)){
    for(j in 1:length(lambda)){
       gh.obj <- ghyp(alpha.bar=a.bar[i],lambda=lambda[j],gamma=-1)
       gh.dens <- dghyp(x.seq,gh.obj)
       gh.dens <- y.range/diff(range(gh.dens))*gh.dens

       lines(x.seq.range+a.bar[i],gh.dens+lambda[j])
       points(a.bar[i],lambda[j])
    }
  }
  legend("topleft",legend=expression(paste("Skewed: ",gamma," = -1")),bty="n",lty="blank")

  plot(0,0,type="n",ylim=range(lambda)+c(-0.5,1),xlim=c(-0.3,max(a.bar)+0.5),
       xlab=expression(bar(alpha)),ylab=expression(lambda))
  for(i in 1:length(a.bar)){
    for(j in 1:length(lambda)){
       gh.obj <- ghyp(alpha.bar=a.bar[i],lambda=lambda[j],gamma=0)
       gh.dens <- log(dghyp(x.seq,gh.obj))
       gh.dens <- y.range/diff(range(gh.dens))*gh.dens

       lines(x.seq.range+a.bar[i],gh.dens+lambda[j])
       points(a.bar[i],lambda[j])
    }
  }
  legend("topleft",legend=expression(paste("Symmetric: ",gamma," = 0")),bty="n",lty="blank")

  plot(0,0,type="n",ylim=range(lambda)+c(-0.5,1),xlim=c(-0.3,max(a.bar)+0.5),
       xlab=expression(bar(alpha)),yaxt="n",ylab="")
  for(i in 1:length(a.bar)){
    for(j in 1:length(lambda)){
       gh.obj <- ghyp(alpha.bar=a.bar[i],lambda=lambda[j],gamma=-1)
       gh.dens <- log(dghyp(x.seq,gh.obj))
       gh.dens <- y.range/diff(range(gh.dens))*gh.dens

       lines(x.seq.range+a.bar[i],gh.dens+lambda[j])
       points(a.bar[i],lambda[j])
    }
  }
  legend("topleft",legend=expression(paste("Skewed: ",gamma," = -1")),bty="n",lty="blank")

  title(main =  "Density and log-Density of the generalized hyperbolic distribution",
        sub=expression(paste("Y-Axis: ", lambda,"; X-Axis: ",bar(alpha),sep="")),
        outer = TRUE, cex.sub=1.2)
@
\caption{The shape of the univariate generalized hyperbolic density drawn
         with different shape parameters $(\lambda,\abar)$. The location and
         scale parameter $\mu$ and $\sigma$ are set to $0$ and $1$, respectively. The
         skewness parameter $\gamma$ is $0$ in the left column and $-1$ in the right
         column of the graphics array.}
\end{center}
\end{figure}
\clearpage
% <---------------------------------------------------------------------->
\section{Modified Bessel function of the third kind}\label{sec:bessel}
% <---------------------------------------------------------------------->
The modified Bessel function of the
third kind appears in the GH as well as in the GIG density (\ref{eq:fghyp}, \ref{eq:densgig}).
This function has the integral representation
\begin{equation}\label{eq:bessel}
  K_\lambda(x) = \OneDivTwo \int_0^\infty w^{\lambda -1}
  \exp\left\{-\frac{1}{2}x\parentheses{w+w^{-1}}\right\}\dd w \hspace{1mm}, \hspace{0.5cm} x > 0.
\end{equation}
The substitution $w = x \sqrt{\chi / \psi}$ can be used to obtain the
following relation, which facilitates to bring the GH
density (\ref{eq:fghyp}) into a closed-form expression.
\begin{equation}\label{eq:besselrelation}
  \int_{0}^{\infty}w^{\lambda-1}\exp\left\{
    -\frac{1}{2}\left(\frac{\chi}{w}+w\psi\right)
  \right\} \dd w =
  2\left(\frac{\chi}{\psi}\right)^\frac{\lambda}{2}
  K_\lambda(\sqrt{\chi\psi})
\end{equation}
When calculating the densities of the special cases of the GH
density we can use the asymtotic relations for small arguments $x$.
\begin{equation}\label{eq:bessellimit1}
  \BesselLambda(x) \sim \Gamma(\lambda)\, 2^{\lambda - 1} x^{-\lambda}
  \quad \hbox{as} \quad x \downarrow 0
  \quad \hbox{and} \quad \lambda > 0
\end{equation}
and
\begin{equation}\label{eq:bessellimit2}
  \BesselLambda(x) \sim \Gamma(-\lambda)\, 2^{-\lambda - 1} x^{\lambda}
  \quad \hbox{as} \quad x \downarrow 0
  \quad \hbox{and} \quad \lambda < 0.
\end{equation}
(\ref{eq:bessellimit2}) follows from (\ref{eq:bessellimit1}) and the observation
that the Bessel function is symmetric with respect to the index
$\lambda$:
\begin{equation}
  \label{eq:bessel-symmetry}
  K_\lambda(x) = K_{-\lambda}(x)
\end{equation}
An asymptotic relation for large arguments $x$ is given by
\begin{equation}\label{eq:bessellimit3}
  \BesselLambda(x) \sim \sqrt{\frac{\pi}{2}} \, x^{- \OneDivTwo} \, e^{-x}
  \quad \hbox{as} \quad x \rightarrow \infty.
\end{equation}
Also, for $\lambda = 0.5$ the Bessel function can be stated
explicitely with
\begin{equation}
  \label{eq:bessel-explicit}
  K_{0.5}(x) = K_{-0.5}(x) = \sqrt{\frac{\pi}{2 x}}e^{-x}, \quad x > 0.
\end{equation}
We refer to \citet*{Abramowitz1972} for further information on Bessel
functions.
\begin{figure}[h]
\begin{center}
\setkeys{Gin}{width=0.7\textwidth}
<<fig=TRUE,echo=FALSE>>=
  lambda <- seq(0.0, 8, length = 5)
  x <- seq(0, 20, length = 101)
  line.type = c("solid", "dotted", "dashed", "dotdash", "longdash")
  par(mfrow = c(1, 2))
  plot(x, besselK(x, lambda[1]), xlab = "x", ylab = expression(K[lambda]), type = "l", lty = line.type[1], log = "")
  for(i in 2:length(lambda)){
    lines(x, besselK(x, lambda[i]), lty = line.type[i])
  }
  legend("topright", legend = lambda, lty = line.type, lwd = 2, title = expression(paste(lambda, "=")))

  plot(x, besselK(x, lambda[1]), xlab = "x", ylab = expression(log(K[lambda])), type = "l", lty = line.type[1], log = "y")
  for(i in 2:length(lambda)){
    lines(x, besselK(x, lambda[i]), lty = line.type[i])
  }
  ##  x <- seq(0.1,1,length=30)
  ##  lambda <- rev(seq(0,2,length=30))
  ##  mod.bessel.3 <- outer(x,lambda)
  ##  for(i in 1:length(lambda)){
  ##    for(j in 1:length(x)){
  ##      mod.bessel.3[j,i] <- ghyp:::.besselM3(x=x[j],lambda=lambda[i])
  ##    }
  ##  }
  ##  persp(x=x,y=rev(lambda),z=mod.bessel.3,theta = 120,ylab="lambda",
  ##        phi = 10,ticktype="detailed")
@
\caption{The modified Bessel function of the third kind drawn with different indices $\lambda$.}
\end{center}
\end{figure}
\clearpage
% <---------------------------------------------------------------------->
\section{Generalized Inverse Gaussian distribution}\label{sec:gig}
% <---------------------------------------------------------------------->
The density of a Generalized Inverse Gaussian (GIG) distribution is
given as
\begin{equation}\label{eq:densgig}
  f_{GIG}(w) = \left(\frac{\psi}{\chi}\right)^{\frac{\lambda}{2}}
  \frac{w^{\lambda-1}}{2K_\lambda(\sqrt{\chi\psi})} \,
  \exp\left\{-\OneDivTwo\left(\frac{\chi}{w}+\psi w
    \right)\right\},
\end{equation}
with parameters satisfying \label{eq:gigconstraints} \\[2ex]
$\chi > 0, \psi \geq 0, \hspace{0.5cm}\lambda < 0 $\\
$\chi > 0, \psi > 0, \hspace{0.5cm}\lambda = 0 $\\
$\chi \geq 0, \psi > 0, \hspace{0.5cm}\lambda > 0 $ .  \customspace
The GIG density nests the inverse Gaussian (IG) density ($\lambda =
-0.5$) and contains many special cases as limits, among others the
Gamma ($\Gamma$) and Inverse Gamma ($\InvGamma$) densities
(cf. \ref{subsec:gamma_dist} and \ref{subsec:inv_gamma_dist} below).

% If $\chi = 0$ and $\lambda > 0$ then $X$ is gamma distributed with
% parameters $\lambda$ and $\OneDivTwo \psi$ ($\Gamma(\lambda,\OneDivTwo
% \psi$)).  If $\psi = 0$ and $\lambda < 0$ then $X$ has an inverse
% gamma distribution with parameters $-\lambda$ and $\OneDivTwo \chi$
% ($\InvGamma(-\lambda,\OneDivTwo \chi$)).

The moment generating function of the GIG distribution
(cf. \citet{Paolella2007}) is determined by
\begin{equation}
  \label{eq:moment-gen-gig}
  \momgen_{GIG}(t) = \parentheses{\frac{\psi}{\psi - 2 t}}^{\lambda / 2}
  \frac{\BesselLambda(\sqrt{\chi (\psi - 2 t)})}{\BesselLambda(\sqrt{\chi \psi})}.
\end{equation}
The $n$-th moment of a GIG distributed random variable $X$
can be found to be
\begin{equation}\label{eq:momentgig}
  \EXP{X^n} = \parentheses{\frac{\chi}{\psi}}^\frac{n}{2}
  \frac{\Bessel{\lambda+n}(\SqrtChiPsi)}{\BesselLambda(\SqrtChiPsi)}.
\end{equation}
Furthermore,
\begin{equation}\label{eq:eloggig}
  \EXP{\ln X} =  \frac{\dd \EXP{X^\alpha}}{\dd \alpha}\biggr|_{\alpha=0}.
\end{equation}
Note that numerical calculations of $\EXP{\ln X}$ may be performed
with the integral representation as well.  In the~\R~package~\ghyp~the
derivative construction is implemented.
% <---------------------------------------------------------------------->
\subsection{Gamma distribution}\label{subsec:gamma_dist}
% <---------------------------------------------------------------------->
When $\chi = 0$ and $\lambda > 0$ the GIG distribution reduces to the
Gamma ($\Gamma$) distribution:
\begin{equation}
  \label{eq:transition_gig_gamma}
  \lim\limits_{\chi \downarrow 0}{f_{GIG}(x |\lambda, \chi, \psi)} =
  f_{\Gamma}(x | \lambda, 1/2 \psi), \quad x \inReal,\, \lambda > 0, \,
  \chi > 0
\end{equation}
The density of $X \sim \Gamma(\alpha, \beta)$ is
\begin{equation}\label{eq:fgamma}
  f_{\Gamma}(w) = \frac{\beta^\alpha}{\Gamma(\alpha)}
  w^{\alpha-1}\exp\curlybrackets{-\beta w}.
\end{equation}
The expected value and the variance are
\begin{equation}
  \EXP{X} = \frac{\beta}{\alpha} \hspace{5mm} \hbox{and} \hspace{5mm}
  \VAR{X} = \frac{\alpha}{\beta^2},
\end{equation}
respectively. The expected value of the logarithm is $\EXP{\ln X} =
\psi(\alpha) - \ln(\beta)$ where $\psi(\cdot)$ is the digamma
function. We will see that this value is not needed to fit a
multivariate variance gamma distribution
(cf. \ref{sec:conddistgamma}).
% <---------------------------------------------------------------------->
\subsection{Inverse gamma distribution}\label{subsec:inv_gamma_dist}
% <---------------------------------------------------------------------->
When $\psi = 0$ and $\lambda < 0$ the GIG distribution reduces to the
Inverse Gamma ($\InvGamma$) distribution:
\begin{equation}
  \label{eq:transition_gig_gamma}
  \lim\limits_{\psi \downarrow 0}{f_{GIG}(x | \lambda, \chi, \psi)} =
  f_{\InvGamma}(x | -\lambda, 1/2 \chi), \quad x \inReal,\, \lambda < 0, \,
  \psi > 0
\end{equation}
The density of $X \sim \InvGamma(\alpha, \beta)$ is
\begin{equation} \label{eq:finversegamma}
  f_{X}(w) = \frac{\beta^\alpha}{\Gamma(\alpha)}
  w^{-\alpha-1}\exp\curlybrackets{-\frac{\beta}{w}}.
\end{equation}
The expected value and the variance are
\begin{equation}\label{eq:einvgamma}
  \EXP{X} = \frac{\beta}{\alpha - 1} \hspace{5mm} \hbox{and} \hspace{5mm}
  \VAR{X} = \frac{\beta^2}{(\alpha - 1)^2(\alpha - 2)},
\end{equation} and exist provided that
$\alpha > 1$ and $\alpha > 2$, respectively. The expected value
of the logarithm is $\EXP{\ln X} = \ln(\beta) - \psi(\alpha)$.
This value is required in order to fit a symmetric multivariate
Student-t distribution by means of the MCECM algorithm (cf. \ref{eq:conddistinversegamma}). \\[4ex]
\begin{figure}[!h]
  \begin{center}
\setkeys{Gin}{width=0.6\textwidth}
<<fig=TRUE,echo=FALSE>>=
  lambda <- -2:2 + 1e-5
  alpha.bar <- 0.5 * 0:3 + 0.01
  x.range <- 0.25
  y.range <- 0.4
  x.seq <- seq(0, 4, length = 101) + 1e-5
  x.seq.range <- x.range / diff(range(x.seq)) * x.seq
  par(mfrow = c(1, 2), omi = 0.5 * c(0, 0.0, 0.8, 0))

  par(mai=c(1,0.8,0,0))
  plot(0, 0, type = "n", ylim = range(lambda) + c(-0.5, 0.5), xlim = c(-0.1, max(alpha.bar) + 0.5),
       xlab = expression(bar(alpha)), ylab = expression(lambda))
  for(i in 1:length(alpha.bar)){
    for(j in 1:length(lambda)){
      tmp <- ghyp:::.abar2chipsi(lambda = lambda[j], alpha.bar = alpha.bar[i])
      gig.dens <- dgig(x.seq, lambda[i], tmp$chi, tmp$psi)
      gig.dens <- y.range/diff(range(gig.dens)) * gig.dens
      lines(x.seq.range + alpha.bar[i], gig.dens + lambda[j])
      points(alpha.bar[i], lambda[j])
    }
  }

  par(mai = c(1, 0, 0, 0.8))
  plot(0, 0, type = "n", ylim = range(lambda) + c(-0.5, 0.5), xlim = c(-0.1, max(alpha.bar) + 0.5),
       xlab = expression(bar(alpha)), yaxt = "n", ylab = "")
  for(i in 1:length(alpha.bar)){
    for(j in 1:length(lambda)){
      tmp <- ghyp:::.abar2chipsi(lambda = lambda[j], alpha.bar = alpha.bar[i])
      gig.dens <- log(dgig(x.seq,lambda[i],tmp$chi,tmp$psi))
      gig.dens <- y.range/diff(range(gig.dens[is.finite(gig.dens)]))*gig.dens
      lines(x.seq.range + alpha.bar[i], gig.dens + lambda[j])
      points(alpha.bar[i], lambda[j])
    }
  }
  title(main = "Density and log-density of the generalized inverse gaussian distribution",
        outer = TRUE, cex.main = 0.8)
@
    \caption{The density and the log-density of the generalized
      inverse gaussian distribution drawn with different shape
      parameters $(\lambda,\abar)$.  See (\ref{eq:abartochipsi}) for
      the transformation from $\abar$ to $(\chi, \psi)$.}
  \end{center}
\end{figure}
\vspace{1cm} \clearpage
% <---------------------------------------------------------------------->
\section{Densities of the special cases of the GH
  distribution}\label{sec:dens_special_cases}
% <---------------------------------------------------------------------->
As mentioned in section \ref{sec:specialcases} the GH
distribution contains several special and limiting cases. In what follows the densities
of the limiting cases are derived.
In the case of a hyperbolic or normal NIG we simply
fix the parameter $\lambda$ either to $(d+1)/2$ or $-0.5$.
% <---------------------------------------------------------------------->
\subsection{Student-t distribution} \label{sec:studentt}
% <---------------------------------------------------------------------->
With relation (\ref{eq:bessellimit2}) it can be easily shown that when
$\psi \rightarrow 0$ and $\lambda < 0$ the density of a GH
distribution results in
\begin{equation}
  f_\X(\x) = \frac{\chi^{-\lambda}
    (\GammaSigGamma)^{\dDivTwo-\lambda}}
  {(2\pi)^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
    \Gamma(-\lambda) 2^{-\lambda - 1}}\times
  \frac{\Bessel{\lambda - \dDivTwo}(
    \sqrt{(\chi + \QX)\GammaSigGamma})
    e^{(\x - \MU)'\InvSigma \GAMMA}}
  {(\sqrt{(\chi + \QX)\GammaSigGamma})^{\dDivTwo - \lambda}}.
\end{equation}
As $\GAMMA \rightarrow 0$ we obtain again with relation
(\ref{eq:bessellimit2}) the symmetric multivariate Student-t density
\begin{equation}
  f_\X(\x) = \frac{\chi^{-\lambda} \Gamma(-\lambda + \dDivTwo)}
  {\pi^{\dDivTwo}\dete{\Sigma}^\OneDivTwo \Gamma(-\lambda)}
  \times
  (\chi + \QX)^{\lambda - \dDivTwo}.
\end{equation}
We switch to the Student-t parametrization and set the degree of
freedom $\nu = -2\lambda$ \footnote{Note that the ($\LambdaAbar$)
  parametrization yields to a slightly different Student-t
  parametrization: In this package the parameter $\Sigma$ denotes the
  variance in the multivariate case and the standard deviation in the
  univariate case. Thus, set $\sigma = \sqrt{\nu/(\nu-2)}$ in the
  univariate case to get the same results as with the
  standard~\R~implementation of the Student-t distribution.  }.
Because $\psi = 0$ the transformation of $\abar$ to $\chi$ and $\psi$
(cf. \ref{eq:abartochipsi}) reduces to
\begin{equation}\label{eq:abartochipsiinvgamma}
  \chi = \abar \; \frac{\BesselLambda(\abar)}
  {\Bessel{\lambda+1}(\abar)}
  \stackrel{\abar\rightarrow0}{\longrightarrow}
  2 \; (-\lambda - 1)
  = \nu - 2.
\end{equation}
Plugging in the values for $\lambda$ and $\nu$, the densities take the
form
\begin{equation}\label{eq:skewtnu}
  f_\X(\x) = \frac{(\nu - 2)^\NuDivTwo (\GammaSigGamma)^{\NuPlusDdivTwo}}
  {(2\pi)^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
    \Gamma(\NuDivTwo) 2^{\NuDivTwo - 1}}\times
  \frac{\Bessel{\NuPlusDdivTwo}(
    \sqrt{(\nu - 2 + \QX)\GammaSigGamma)}
    \; e^{(\x - \MU)'\InvSigma \GAMMA}}
  {(\sqrt{(\nu - 2 + \QX)\GammaSigGamma})^{\NuPlusDdivTwo}}
\end{equation}
and for the symmetric case as $\GAMMA \rightarrow 0$
\begin{equation}
  f_\X(\x) = \frac{(\nu - 2)^\NuDivTwo \Gamma(\NuPlusDdivTwo)}
  {\pi^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
    \Gamma(\NuDivTwo) (\nu - 2 + \QX)^{\NuPlusDdivTwo}}.
\end{equation}
It is important to note that the variance does not exist in the
symmetric case for $\nu \leq 2$ while for the asymmetric case the
variance does not exist for $\nu \leq 4$. This is because the variance
of an asymmetric GH distribution involves the variance of the mixing
distribution.  In case of a Student-t distribution, the mixing
variable $w$ is inverse gamma distributed and has finite variance only
if $\beta > 2$ which corresponds to $\lambda < -2$, i.e. $\nu > 4$
(cf. \ref{eq:einvgamma}).  Alternatively, in the univariate case, this
can be seen by the fact that the Student-t density has regularly
varying tails. For $x \rightarrow \infty$, one obtains
\begin{eqnarray}
  f_X(x) &=& L(x)\,x^{-\nu - 1}, \hspace{5mm} \hbox{for} \, \gamma = 0 \\
  f_X(x) &=& L(x)\,x^{-\frac{\nu}{2} - 1}, \hspace{5mm} \hbox{for} \, \gamma > 0, \label{eq:skewttail}
\end{eqnarray}
where $L(x)$ denotes a slowly varying function at $\infty$.  The
asymptotic relation for the modified Bessel function of the third kind
(\ref{eq:bessellimit3}) was applied to (\ref{eq:skewtnu}) to arrive at
(\ref{eq:skewttail}).
% <---------------------------------------------------------------------->
\subsection{Variance gamma distribution}
% <---------------------------------------------------------------------->
Relation (\ref{eq:bessellimit1}) can be used again to show that
for $\chi \rightarrow 0$ and $\lambda > 0$ the density of the
GH distribution results in
\begin{equation}
  f_\X(\x) = \frac{\psi^{\lambda}
            (\psi + \GammaSigGamma)^{\dDivTwo-\lambda}}
            {(2\pi)^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
            \Gamma(\lambda) 2^{\lambda - 1}}\times
            \frac{\Bessel{\lambda - \dDivTwo}(
            \sqrt{\QX (\psi +\GammaSigGamma))}
            \; e^{(\x - \MU)'\InvSigma \GAMMA}}
            {(\sqrt{\QX (\psi +\GammaSigGamma)})^{\dDivTwo - \lambda}}.
\end{equation}
In the case of a variance gamma distribution the transformation of $\abar$ to $\chi$ and $\psi$
(cf. \ref{eq:abartochipsi}) reduces to
\begin{equation}\label{eq:abartochipsigamma}
  \psi = \abar \; \frac{\Bessel{\lambda+1}(\abar)}
         {\BesselLambda(\abar)}
       = 2 \lambda
\end{equation}
Thus, the density is
\begin{equation}
  f_\X(\x) = \frac{2 \, \lambda^{\lambda}
            (2 \lambda + \GammaSigGamma)^{\dDivTwo-\lambda}}
            {(2\pi)^{\dDivTwo}\dete{\Sigma}^\OneDivTwo
            \Gamma(\lambda)}\times
            \frac{\Bessel{\lambda - \dDivTwo}(
            \sqrt{\QX (2 \lambda +\GammaSigGamma))}
            \; e^{(\x - \MU)'\InvSigma \GAMMA}}
            {(\sqrt{\QX (2 \lambda +\GammaSigGamma)})^{\dDivTwo - \lambda}}.
\end{equation}
% Second limiting case:
A limiting case arises when $\QX \rightarrow 0$, that is when $\x - \MU \rightarrow 0$.
As long as $\lambda - \dDivTwo > 0$
relation (\ref{eq:bessellimit1}) can be used to verify that the density reduces to
\begin{equation}
  f_\X(\x) = \frac{\psi^{\lambda}
            (\psi + \GammaSigGamma)^{\dDivTwo-\lambda} \, \Gamma(\lambda - \dDivTwo)}
            {2^d \, \pi^{\dDivTwo} \, \dete{\Sigma}^\OneDivTwo \, \Gamma(\lambda)}.
\end{equation}
By replacing $\psi$ with $2\lambda$ the limiting density is obtained in the
($\LambdaAbar$)-parametrization.
\footnote{The numeric implementation in~\R~uses
spline interpolation for the case where $\lambda - \dDivTwo > 0$ and $\QX < \epsilon$.}\\[2ex]
For $\lambda - \dDivTwo \leq 0$  the density diverges. \footnote{The current
workaround in~\R~simply sets observations where $\QX < \epsilon$ to
$\epsilon$ when $\lambda - \dDivTwo \leq 0$.}
% <---------------------------------------------------------------------->
\section{Conditional density of the mixing variable $W$}
% <---------------------------------------------------------------------->
Performing the E-Step of the MCECM algorithm requires the calculation
of the conditional expectation of $w_i$ given $\x_i$. In this section
the conditional density is derived.
% <---------------------------------------------------------------------->
\subsection{Generalized hyperbolic, hyperbolic and NIG distribution}
% <---------------------------------------------------------------------->
The mixing term $w$ is GIG distributed. By using (\ref{eq:fghyp}) and
(\ref{eq:densgig}) the density of $w_i$ given $\x_i$ can be calculated to
be again the GIG density with parameters
$\left(\lambda - \dDivTwo,\QX + \chi, \psi + \GammaSigGamma\right)$.
\begin{eqnarray}\label{eq:conddistGIG}
  f_{w|\x}(w) &=& \nonumber
                  \frac{f_{\X,W}(\x,w)}{f_\X(\x)}\\
              &=& \nonumber
                  \frac{f_{\X|W}(\x)f_{GIG}(w)}{\int_0^\infty{f_{\X|W}(\x)f_{GIG}(w)\dd w}}\\
              &=& \nonumber
                  \parentheses{\frac{\PsiTransfGIG}{\ChiTransfGIG}}^{0.5 ( \LambdaTransfGIG) } \times \\
              &&
                  \frac{w^{\LambdaTransfGIG-1}
                  \exp\left\{-\OneDivTwo\left(\frac{\ChiTransfGIG}{w}+
                  w \, (\PsiTransfGIG)  \right)\right\}}
                  {2 \, K_{\LambdaTransfGIG}(\sqrt{(\ChiTransfGIG)\,(\PsiTransfGIG}))} \,
\end{eqnarray}
% <---------------------------------------------------------------------->
\subsection{Student-t distribution}
% <---------------------------------------------------------------------->
The mixing term $w$ is $\InvGamma$ distributed. Again, the conditional density
of $w_i$ given $\x_i$ results in the GIG density. The equations (\ref{eq:fghyp}) and
(\ref{eq:finversegamma}) were used. The parameters of the GIG density are
$(\lambda - \dDivTwo,\QX + \chi, \GammaSigGamma)$. When $\gamma$ becomes $0$ the
conditional density reduces to the $\InvGamma$ density with parameters
$\left(\dDivTwo - \lambda,\frac{\QX + \chi}{2}\right)$.
\begin{eqnarray}\label{eq:conddistinversegamma}
  f_{w|\x}(w) &=& \nonumber
                  \frac{f_{\X,W}(\x,w)}{f_\X(\x)}\\
              &=& \nonumber
                  \frac{f_{\X|W}(\x)f_{\InvGamma}(w)}{\int_0^\infty{f_{\X|W}(\x)f_{\InvGamma}(w)\dd w}}\\
              &=& \parentheses{\frac{\GammaSigGamma}{\ChiTransfGIG}}^{0.5 ( \LambdaTransfGIG)} \times
                  \frac{w^{\LambdaTransfGIG-1}
                  \exp\left\{-\OneDivTwo\left(\frac{\ChiTransfGIG}{w}+
                  w \, \GammaSigGamma  \right)\right\}}
                  {2 \, K_{\LambdaTransfGIG}(\sqrt{(\ChiTransfGIG) \, \GammaSigGamma})} \,
\end{eqnarray}
% <---------------------------------------------------------------------->
\subsection{Variance gamma distribution}\label{sec:conddistgamma}
% <---------------------------------------------------------------------->
The mixing term $w$ is $\Gamma$ distributed. By using (\ref{eq:fghyp}) and
(\ref{eq:fgamma}) the density of $w_i$ given $\x_i$ can be calculated to
be again the GIG density with parameters
$\left(\lambda - \dDivTwo,\QX, \psi + \GammaSigGamma \right)$.
\begin{eqnarray}\label{eq:conddistgamma}
  f_{w|\x}(w) &=& \nonumber
                  \frac{f_{\X,W}(\x,w)}{f_\X(\x)}\\
              &=& \nonumber
                  \frac{f_{\X|W}(\x)f_{\Gamma}(w)}{\int_0^\infty{f_{\X|W}(\x)f_{\Gamma}(w)\dd w}}\\
              &=& \parentheses{\frac{\PsiTransfGIG}{\QX}}^{0.5 (\LambdaTransfGIG)} \times \\
              &&  \frac{w^{ \LambdaTransfGIG-1}
                  \exp\left\{-\OneDivTwo \left(\frac{\QX}{w}+
                  w \, (\PsiTransfGIG)  \right)\right\}}
                  {2 \, K_{\LambdaTransfGIG}(\sqrt{\QX \, (\PsiTransfGIG}))} \,
\end{eqnarray}
% <---------------------------------------------------------------------->
\section{Distribution objects}
% <---------------------------------------------------------------------->
In the package~\ghyp~we follow an object-oriented programming approach
and introduce distribution objects. There are mainly four reasons for
that: \renewcommand{\labelenumi}{\arabic{enumi}.}
\begin{enumerate}
\item Unlike most distributions the GH distribution involves at least
  5 parameters which have to fulfill some consistency
  requirements. Consistency checks can be performed once for all when
  an object is initialized.  In addition, constructors for different
  parametrizations can be added easily and do not necessitate a change
  of all the function headers.
\item Once initialized the common functions belonging to a
  distribution can be called conveniently by passing the distribution
  object. A repeated input of the parameters is avoided.
\item Distribution objects returned from fitting procedures can be
  directly passed to, e.g., the density function since fitted
  distribution objects add information to the distribution object and
  consequently inherit from the class of the distribution object.
\item Generic method dispatching can be used to provide a uniform
  interface to, e.g., calculate the expected value
  \verb=mean(distribution.object)=.  Additionally, one can take
  advantage of generic programming since~\R~provides virtual classes
  and some forms of polymorphism.
\end{enumerate}
See appendix \ref{sec:example} for several examples and
\ref{sec:exampleobjoriented} for particular examples concerning the
object-oriented approach.

% <---------------------------------------------------------------------->
\section{Constructors}\label{sec:constructors}
% <---------------------------------------------------------------------->
Before giving examples on how GH distribution objects can be
initialized, let us list the constructor functions for different
distributions and parametrizations. Note that the constructors below
are used to initialize both, univariate and multivariate
distributions.

\begin{center}
  \begin{tabular}{c|c|c|c|}
    & \multicolumn{3}{|c|}{Parametrization} \\
    \hline
    Distribution & $(\LambdaChiPsi)$ & $(\LambdaAbar)$ &
    $(\BarndorffParam)$ \\
    \hline
    GH & \verb\ghyp(...)\ & \verb\ghyp(..., alpha.bar=x)\ & \verb\ghyp.ad(...)\ \\
    hyp & \verb\hyp(...)\ & \verb\hyp(..., alpha.bar=x)\ & \verb\hyp.ad(...)\ \\
    NIG & \verb\NIG(...)\ & \verb\NIG(..., alpha.bar=x)\ & \verb\NIG.ad(...)\ \\
    Student-t & \verb\student.t(..., chi=x)\ & \verb\student.t(...)\  &
    \verb\student.t.ad(...)\ \\
    VG & \verb\VG(..., psi=x)\ & \verb\VG(...)\  & \verb\VG.ad(...)\
  \end{tabular}
\end{center}

Apparently, the same constructor functions are used for the
$(\LambdaChiPsi)$ and the $(\LambdaAbar)$ parametrization.

In case of the GH, hyp, and NIG distribution, the idea is that as long
as the parameter \verb=alpha.bar= is not submitted the
$(\LambdaChiPsi)$ parametrization is used. Conversely, if
\verb=alpha.bar= is submitted, the $(\LambdaAbar)$ parametrization
will be used and the parameters $\chi$ and $\psi$ are neglected. Thus,
typing either \verb=ghyp()=, \verb=hyp()=, or \verb=NIG()= initializes
an distribution object in the $(\LambdaChiPsi)$ parametrization.

This is different for the Student-t and the VG distribution. Per
default, these distributions are initialized in the $(\LambdaAbar)$
parametrization. In case of the Student-t distribution, the
$(\LambdaChiPsi)$ parametrization is choosen as soon as a $\chi$
(\verb=chi=) is submitted which does not satisfy
eq. \ref{eq:abartochipsiinvgamma} In case of the VG distribution, the
$(\LambdaChiPsi)$ parametrization is choosen as soon as a $\psi$
(\verb=psi=) is submitted which does not satisfy
eq. \ref{eq:abartochipsigamma}

To get the standard Student-t parametrization (i.e. the R-core
implementation) use \\ \verb\student.t(nu = nu, chi = nu)\.

% <---------------------------------------------------------------------->
\section{Examples}\label{sec:example}
% <---------------------------------------------------------------------->
This section provides examples of distribution objects and the object-oriented
approach as well as fitting to data and
portfolio optimization.

% <---------------------------------------------------------------------->
\subsection{Initializing distribution objects}\label{sec:exampledistobj}
% <---------------------------------------------------------------------->
This example shows how GH distribution objects can be initialized by either using
the $(\LambdaChiPsi)$, the $(\LambdaAbar)$ or the $(\BarndorffParam)$-parametrization.
<<>>=
  ## Load the package "ghyp" and the data "smi.stocks" first
  library(ghyp)
  ## Initialized a univariate GH distribution object with
  ## the lambda/alpha.bar parametrization
  ghyp(lambda=-2, alpha.bar=0.5, mu=10, sigma=5, gamma=1)
  ghyp.ad(lambda=-2, alpha=1, beta = 0.5, mu=10, delta=1)
  ## Initialized a multivariate GH distribution object with
  ## the lambda/chi/psi parametrization
  ghyp(lambda=-2, chi=5, psi=0.1, mu=10:11, sigma=diag(5:6), gamma=-1:0)
@
% <---------------------------------------------------------------------->
\subsection{Object-oriented approach}\label{sec:exampleobjoriented}
% <---------------------------------------------------------------------->
First of all a GH distribution object is initialized and a consistency check takes place.
The second command shows how the initialized distribution object is passed to the
density function. Then a Student-t distribution is fitted to the daily log-returns
of the Novartis stock. The fitted distribution object is passed to the quantile
function. Since the fitted distribution object inherits from the distribution object
this constitutes no problem.\\
The generic methods \emph{hist}, \emph{pairs}, \emph{coef},
\emph{plot}, \emph{lines}, \emph{transform}, \emph{[.},
\emph{mean} and \emph{vcov} are defined for distribution objects
inheriting from the class \verb=ghyp=.\\
The generic methods \emph{logLik}, \emph{AIC} and \emph{summary} are
added for the class \verb=mle.ghyp=,
inheriting from the class \verb=ghyp=.
\begin{Schunk}
\begin{Sinput}
  ## Consistency check when initializing a GH distribution object.
  ## Valid input:
  univariate.ghyp.object <- student.t(nu = 3.5, mu = 10,
                                      sigma = 5, gamma = 1)

  ## Passing a distribution object to the density function
  dghyp(10:14,univariate.ghyp.object)

  ## Passing a fitted distribution object to the quantile function
  fitted.ghyp.object <- fit.tuv(smi.stocks[,"Novartis"], silent = TRUE)
  qghyp(c(0.01,0.05),fitted.ghyp.object)

  ## Generic method dispatching: the histogram method
  hist(fitted.ghyp.object, legend.cex = 0.7)

  ## Generic programming:
  mean(fitted.ghyp.object)     ## fitted.ghyp.object is of class
                               ## "mle.ghyp" which extends "ghyp"

  vcov(univariate.ghyp.object)
\end{Sinput}
\end{Schunk}
% <---------------------------------------------------------------------->
\subsection{Fitting generalized hyperbolic distributions to data}
% <---------------------------------------------------------------------->
A multivariate NIG distribution is fitted to the
daily returns of three swiss blue chips:
Credit Suisse, Nestle and Novartis.
A \emph{pairs} plot is drawn in order to
do some graphical diagnostics of the accuracy of the fit.
\setkeys{Gin}{width=\textwidth}
<<fig=TRUE>>=
 data(smi.stocks)
 fitted.returns.mv <- fit.NIGmv(data=smi.stocks[1:500,c("CS","Nestle","Novartis")],
                                 silent=TRUE)
 pairs(fitted.returns.mv, cex=0.5, nbins=20)
@
\\[3ex]
In the following part daily log-returns of the SMI are fitted to the
hyperbolic distribution. Again, some graphical verification is done to check the
accuracy of the fit.
\setkeys{Gin}{width=\textwidth}
<<fig=TRUE,width=8,height=4>>=
  fitted.smi.returns <- fit.hypuv(data=smi.stocks[,c("SMI")],silent=TRUE)
  par(mfrow=c(1,3))
  hist(fitted.smi.returns,ghyp.col="blue",legend.cex=0.7)
  hist(fitted.smi.returns,log.hist=T,nclass=30,plot.legend=F,ghyp.col="blue")
  qqghyp(fitted.smi.returns,plot.legend=T,legend.cex=0.7)
@
% <---------------------------------------------------------------------->
\end{appendix}
% <---------------------------------------------------------------------->
\bibliographystyle{plainnat}
\bibliography{ghypbib}
% <---------------------------------------------------------------------->
\end{document}
% <---------------------------------------------------------------------->
