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  %\VignetteIndexEntry{Risk and ruin theory}
  %\VignettePackage{actuar}

  \title{Risk theory features of \pkg{actuar}}
  \author{Christophe Dutang \\ ISFA, Université Claude Bernard Lyon 1 \\[3ex]
    Vincent Goulet \\ École d'actuariat, Université Laval \\[3ex]
    Mathieu Pigeon \\ École d'actuariat, Université Laval}
  \date{}

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  \newcommand{\E}[1]{E[ #1 ]}
  \newcommand{\VAR}[1]{\mathrm{Var} [ #1 ]}
  \newcommand{\VaR}{\mathrm{VaR}}
  \newcommand{\CTE}{\mathrm{CTE}}
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<<echo=FALSE>>=
library(actuar)
options(width = 62, digits = 4)
@

\begin{document}

\maketitle

\section{Introduction}
\label{sec:introduction}

Risk theory refers to a body of techniques to model and measure the
risk associated with a portfolio of insurance contracts. A first
approach consists in modeling the distribution of total claims over a
fixed period of time using the classical collective model of risk
theory. A second input of interest to the actuary is the evolution of
the surplus of the insurance company over many periods of time. In
\emph{ruin theory}, the main quantity of interest is the probability
that the surplus becomes negative, in which case technical ruin of the
insurance company occurs.

The interested reader can read more on these subjects in
\cite{LossModels3e,Gerber_MRT,DenuitCharpentier1,MART}, among others.

The current version of \pkg{actuar} \citep{actuar} contains four
visible functions related to the above problems: two for the
calculation of the aggregate claim amount distribution and two for
ruin probability calculations.



\section{The collective risk model}
\label{sec:collective-risk-model}

Let  random variable $S$ represent the aggregate claim amount (or
total amount of claims) of a portfolio of independent risks over a
fixed period of time, random variable $N$ represent the number of
claims (or frequency) in the portfolio over that period, and random
variable $C_j$ represent the amount of claim $j$ (or severity). Then,
we have the random sum
\begin{equation}
  \label{eq:definition-S}
  S = C_1 + \dots + C_N,
\end{equation}
where we assume that $C_1, C_2, \dots$ are mutually independent and
identically distributed random variables each independent of $N$.
The task at hand consists in evaluating numerically the cdf of $S$,
given by
\begin{align}
  \label{eq:cdf-S}
  F_S(x)
  &= \Pr[S \leq x] \notag \\
  &= \sum_{n = 0}^\infty \Pr[S \leq x|N = n] p_n \notag \\
  &= \sum_{n = 0}^\infty  F_C^{*n}(x) p_n,
\end{align}
where $F_C(x) = \Pr[C \leq x]$ is the common cdf of $C_1, \dots, C_n$,
$p_n = \Pr[N = n]$ and $F_C^{*n}(x) = \Pr[C_1 + \dots + C_n \leq x]$
is the $n$-fold convolution of $F_C(\cdot)$. If $C$ is discrete on $0,
1, 2, \dots$, one has
\begin{equation}
  \label{eq:convolution-formula}
  F_C^{*k}(x) =
  \begin{cases}
    I\{x \geq 0\}, & k = 0 \\
    F_C(x),        & k = 1 \\
    \sum_{y = 0}^x F_C^{*(k - 1)}(x - y) f_C(y), & k = 2, 3, \dots,
  \end{cases}
\end{equation}
where $I\{\mathcal{A}\} = 1$ if $\mathcal{A}$ is true and
$I\{\mathcal{A}\} = 0$ otherwise.



\section{Discretization of claim amount distributions}
\label{sec:discretization}

Some numerical techniques to compute the aggregate claim amount
distribution (see Section~\ref{sec:aggregate}) require a
discrete arithmetic claim amount distribution; that is, a distribution
defined on $0, h, 2h, \dots$ for some step (or span, or lag) $h$. The
package provides function \code{discretize} to discretize a
continuous distribution. (The function can also be used to modify the
support of an already discrete distribution, but this requires
additional care.)

Let $F(x)$ denote the cdf of the distribution to discretize on some
interval $(a, b)$ and $f_x$ denote the probability mass at $x$ in the
discretized distribution. Currently, \code{discretize} supports the
following four discretization methods.
\begin{enumerate}
\item Upper discretization, or forward difference of $F(x)$:
  \begin{equation}
    \label{eq:discretization:upper}
    f_x = F(x + h) - F(x)
  \end{equation}
  for $x = a, a + h, \dots, b - h$. The discretized cdf is always
  above the true cdf.
\item Lower discretization, or backward difference of $F(x)$:
  \begin{equation}
    \label{eq:discretization:lower}
    f_x =
    \begin{cases}
      F(a),            & x = a \\
      F(x) - F(x - h), & x = a + h, \dots, b.
    \end{cases}
  \end{equation}
  The discretized cdf is always under the true cdf.
\item Rounding of the random variable, or the midpoint method:
  \begin{equation}
    \label{eq:discretization:midpoint}
    f_x =
    \begin{cases}
      F(a + h/2),              & x = a \\
      F(x + h/2) - F(x - h/2), & x = a + h, \dots, b - h.
    \end{cases}
  \end{equation}
  The true cdf passes exactly midway through the steps of the
  discretized cdf.
\item Unbiased, or local matching of the first moment method:
  \begin{equation}
    \label{eq:discretization:unbiased}
    f_x =
    \begin{cases}
      \dfrac{\E{X \wedge a} - \E{X \wedge a + h}}{h} + 1 - F(a),
      & x = a \\
      \dfrac{2 \E{X \wedge x} - \E{X \wedge x - h}
        - \E{X \wedge x + h}}{h},
      & a < x < b \\
      \dfrac{\E{X \wedge b} - \E{X \wedge b - h}}{h} - 1 + F(b), &
      x = b.
    \end{cases}
  \end{equation}
  The discretized and the true distributions have the same total
  probability and expected value on $(a, b)$.
\end{enumerate}

Figure \ref{fig:discretization-methods} illustrates the four methods.
It should be noted that although very close in this example, the
rounding and unbiased methods are not identical.
\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
fu <- discretize(plnorm(x), method = "upper", from = 0, to = 5)
fl <- discretize(plnorm(x), method = "lower", from = 0, to = 5)
fr <- discretize(plnorm(x), method = "rounding", from = 0, to = 5)
fb <- discretize(plnorm(x), method = "unbiased", from = 0, to = 5,
                 lev = levlnorm(x))
par(mfrow = c(2, 2), c(5, 2, 4, 2))
curve(plnorm(x), from = 0, to = 5, lwd = 2, main = "Upper", ylab = "F(x)")
plot(stepfun(0:4, diffinv(fu)), pch = 20, add = TRUE)
curve(plnorm(x), from = 0, to = 5, lwd = 2, main = "Lower", ylab = "F(x)")
plot(stepfun(0:5, diffinv(fl)), pch = 20, add = TRUE)
curve(plnorm(x), from = 0, to = 5, lwd = 2, main = "Rounding", ylab = "F(x)")
plot(stepfun(0:4, diffinv(fr)), pch = 20, add = TRUE)
curve(plnorm(x), from = 0, to = 5, lwd = 2, main = "Unbiased", ylab = "F(x)")
plot(stepfun(0:5, diffinv(fb)), pch = 20, add = TRUE)
## curve(plnorm(x), from = 0, to = 5, lwd = 2, ylab = "F(x)")
## par(col = "blue")
## plot(stepfun(0:4, diffinv(fu)), pch = 19, add = TRUE)
## par(col = "red")
## plot(stepfun(0:5, diffinv(fl)), pch = 19, add = TRUE)
## par(col = "green")
## plot(stepfun(0:4, diffinv(fr)), pch = 19, add = TRUE)
## par(col = "magenta")
## plot(stepfun(0:5, diffinv(fb)), pch = 19, add = TRUE)
## legend(3, 0.3, legend = c("upper", "lower", "rounding", "unbiased"),
##        col = c("blue", "red", "green", "magenta"), lty = 1, pch = 19,
##        text.col = "black")
@
  \caption{Comparison of four discretization methods}
  \label{fig:discretization-methods}
\end{figure}

Usage of \code{discretize} is similar to \proglang{R}'s plotting
function \code{curve}. The cdf to discretize and, for the unbiased
method only, the limited expected value function are passed to
\code{discretize} as expressions in \code{x}. The other arguments
are the upper and lower bounds of the discretization interval, the
step $h$ and the discretization method. For example, upper and
unbiased discretizations of a Gamma$(2, 1)$ distribution on $(0, 17)$
with a step of $0.5$ are achieved with, respectively,
<<echo=TRUE, eval=FALSE>>=
fx <- discretize(pgamma(x, 2, 1), method = "upper",
                 from = 0, to = 17, step = 0.5)
fx <- discretize(pgamma(x, 2, 1), method = "unbiased",
                 lev = levgamma(x, 2, 1),
                 from = 0, to = 17, step = 0.5)
@

Function \code{discretize} is written in a modular fashion making it
simple to add other discretization methods if needed.


\section{Calculation of the aggregate claim amount distribution}
\label{sec:aggregate}

Function \code{aggregateDist} serves as a unique front end for
various methods to compute or approximate the cdf of the aggregate
claim amount random variable $S$. Currently, five methods are
supported.
\begin{enumerate}
\item Recursive calculation using the algorithm of \cite{Panjer_81}.
  This requires the severity distribution to be discrete arithmetic on
  $0, 1, 2, \dots, m$ for some monetary unit and the frequency
  distribution to be a member of either the $(a, b, 0)$ or $(a, b, 1)$
  family of distributions \citep{LossModels3e}. (These families
  contain the Poisson, binomial, negative binomial and logarithmic
  distributions and their extensions with an arbitrary mass at $x =
  0$.) The general recursive formula is:
  \begin{displaymath}
    f_S(x) = \frac{(p_1 - (a + b)p_0)f_C(x) + \sum_{y=1}^{\min(x,
        m)}(a + by/x)f_C(y)f_S(x - y)}{1 - a f_C(0)},
  \end{displaymath}
  with starting value $f_S(0) = P_N(f_C(0))$, where $P_N(\cdot)$ is
  the probability generating function of $N$. Probabilities are
  computed until their sum is arbitrarily close to 1.

  The recursions are done in \proglang{C} to dramatically increase
  speed. One difficulty the programmer is facing is the unknown length
  of the output. This was solved using a common, simple and fast
  technique: first allocate an arbitrary amount of memory and double
  this amount each time the allocated space gets full.

\item Exact calculation by numerical convolutions using
  \eqref{eq:cdf-S} and \eqref{eq:convolution-formula}. This
  also requires a discrete severity distribution. However, there is no
  restriction on the shape of the frequency distribution. The package
  merely implements the sum \eqref{eq:cdf-S}, the convolutions being
  computed with \proglang{R}'s function \code{convolve}, which in turn
  uses the Fast Fourier Transform. This approach
  is practical for small problems only, even on today's fast
  computers.
\item Normal approximation of the cdf, that is
  \begin{equation}
    \label{eq:normal-approximation}
    F_S(x) \approx \Phi
    \left(
      \frac{x - \mu_S}{\sigma_S}
    \right),
  \end{equation}
  where $\mu_S = \E{S}$ and $\sigma_S^2 = \VAR{S}$. For most realistic
  models, this approximation is rather crude in the tails of the
  distribution.
\item Normal Power II approximation of the cdf, that is
  \begin{equation}
    \label{eq:np2-approximation}
    F_S(x) \approx \Phi
    \left(
      -\frac{3}{\gamma_S} +
      \sqrt{\frac{9}{\gamma_S^2} + 1
        + \frac{6}{\gamma_S}
        \frac{x - \mu_S}{\sigma_S}} \right),
  \end{equation}
  where $\gamma_S = \E{(S - \mu_S)^3}/\sigma_S^{3/2}$. The
  approximation is valid for $x > \mu_S$ only and performs reasonably
  well when $\gamma_S < 1$. See \cite{Daykin_et_al} for details.
\item Simulation of a random sample from $S$ and approximation of
  $F_S(x)$ by the empirical cdf
  \begin{equation}
    F_n(x) = \frac{1}{n} \sum_{j = 1}^n I\{x_j \leq x\}.
  \end{equation}
  The simulation itself is done with function \code{simul} (see the
  \code{"simulation"} vignette)). This function admits very general
  hierarchical models for both the frequency and the severity
  components.
\end{enumerate}

Here also, adding other methods to \code{aggregateDist} is simple due
to its modular conception.

The arguments of \code{aggregateDist} differ depending on the
calculation method; see the help page for details. One interesting
argument to note is \code{x.scale} to specify the monetary unit of
the severity distribution. This way, one does not have to mentally do
the conversion between the support of $0, 1, 2, \dots$ assumed by the
recursive and convolution methods and the true support of $S$.

The recursive method fails when the expected number of claims is so
large that $f_S(0)$ is numerically equal to zero. One solution
proposed by \citet{LossModels3e} consists in dividing the appropriate
parameter of the frequency distribution by $2^n$, with $n$ such that
$f_S(0) > 0$ and the recursions can start. One then computes the
aggregate claim amount distribution using the recursive method and
then convolves the resulting distribution $n$ times with itself to
obtain the final distribution. Function \code{aggregateDist} supports
this procedure through its argument \code{convolve}.

The function returns an object of class \code{"aggregateDist"}
inheriting from the \code{"function"} class. Thus, one can use the
object as a function to compute the value of $F_S(x)$ in any $x$.

For illustration purposes, consider the following model: the
distribution of $S$ is a compound Poisson with parameter $\lambda =
10$ and severity distribution Gamma$(2, 1)$. To obtain an
approximation of the cdf of $S$ we first discretize the gamma
distribution on $(0, 22)$ with the unbiased method and a step of
$0.5$, and then use the recursive method in \code{aggregateDist}:
<<echo=TRUE>>=
fx <- discretize(pgamma(x, 2, 1), from = 0, to = 22, step = 0.5,
                 method = "unbiased", lev = levgamma(x, 2, 1))
Fs <- aggregateDist("recursive", model.freq = "poisson",
                    model.sev = fx, lambda = 10, x.scale = 0.5)
summary(Fs)                           # summary method
@
Although useless here, the following is essentially equivalent, except
in the far right tail for numerical reasons:
<<echo=TRUE>>=
Fsc <- aggregateDist("recursive", model.freq = "poisson",
                     model.sev = fx, lambda = 5, convolve = 1, x.scale = 0.5)
summary(Fsc)                           # summary method
@

We return to object \code{Fs}. It contains an empirical cdf with support
<<echo=TRUE>>=
knots(Fs)                             # support of Fs.b (knots)
@
A nice graph of this function is obtained with a method of \code{plot} (see
Figure \ref{fig:Fs}):
<<echo=TRUE, eval=FALSE>>=
plot(Fs, do.points = FALSE, verticals = TRUE, xlim = c(0, 60))
@
\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
plot(Fs, do.points = FALSE, verticals = TRUE, xlim = c(0, 60))
@
  \caption{Graphic of the empirical cdf of $S$ obtained with the
    recursive method}
  \label{fig:Fs}
\end{figure}

The package defines a few summary methods to extract information from
\code{"aggregateDist"} objects. First, there are methods of
\code{mean} and \code{quantile} to easily compute the mean and
obtain the quantiles of the approximate distribution:
<<echo=TRUE>>=
mean(Fs)                              # empirical mean
quantile(Fs)                          # quantiles
quantile(Fs, 0.999)                   # quantiles
@

Second, a method of \texttt{diff} gives easy access to the underlying
probability mass function:
<<echo=TRUE>>=
diff(Fs)
@
Of course, this is defined (and makes sense) for the recursive, direct
convolution and simulation methods only.

Third, the package introduces the generic functions \code{VaR} and
\code{CTE} (with alias \code{TVaR}) with methods for objects of class
\code{"aggregateDist"}. The former computes the value-at-risk
$\VaR_\alpha$ such that
\begin{equation}
  \label{eq:VaR}
  \Pr[S \leq \VaR_\alpha] = \alpha,
\end{equation}
where $\alpha$ is the confidence level. Thus, the value-at-risk is
nothing else than a quantile. As for the method of \code{CTE}, it
computes the conditional tail expectation (also called Tail
Value-at-Risk)
\begin{equation}
  \label{eq:CTE}
  \CTE_\alpha = \E{S|S > \VaR_\alpha}.
\end{equation}
Here are examples using object \code{Fs} obtained above:
<<echo=TRUE>>=
VaR(Fs)
CTE(Fs)
@

To conclude on the subject, Figure \ref{fig:Fs-comparison} shows the
cdf of $S$ using five of the many combinations of discretization and
calculation method supported by \pkg{actuar}.
\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
fx.u <- discretize(pgamma(x, 2, 1), from = 0, to = 22, step = 0.5,
                   method = "upper")
Fs.u <- aggregateDist("recursive", model.freq = "poisson",
                      model.sev = fx.u, lambda = 10, x.scale = 0.5)
fx.l <- discretize(pgamma(x, 2, 1), from = 0, to = 22, step = 0.5,
                   method = "lower")
Fs.l <- aggregateDist("recursive", model.freq = "poisson",
                      model.sev = fx.l, lambda = 10, x.scale = 0.5)
Fs.n <- aggregateDist("normal", moments = c(20, 60))
Fs.s <- aggregateDist("simulation",
                      model.freq = expression(y = rpois(10)),
                      model.sev = expression(y = rgamma(2, 1)),
                      nb.simul = 10000)
par(col = "black")
plot(Fs, do.points = FALSE, verticals = TRUE, xlim = c(0, 60), sub = "")
par(col = "blue")
plot(Fs.u, do.points = FALSE, verticals = TRUE, add = TRUE, sub = "")
par(col = "red")
plot(Fs.l, do.points = FALSE, verticals = TRUE, add = TRUE, sub = "")
par(col = "green")
plot(Fs.s, do.points = FALSE, verticals = TRUE, add = TRUE, sub = "")
par(col = "magenta")
plot(Fs.n, add = TRUE, sub = "")
legend(30, 0.4, c("recursive + unbiased", "recursive + upper", "recursive + lower", "simulation", "normal approximation"),
       col = c("black", "blue", "red", "green", "magenta"),
       lty = 1, text.col = "black")
@
  \caption{Comparison between the empirical or approximate cdf of $S$
    obtained with five different methods}
  \label{fig:Fs-comparison}
\end{figure}


\section{The continuous time ruin model}
\label{sec:ruin-model}

We now turn to the multi-period ruin problem. Let $U(t)$ denote the
surplus of an insurance company at time $t$, $c(t)$ denote premiums
collected through time $t$, and $S(t)$ denote aggregate claims paid
through time $t$. If $u$ is the initial surplus at time $t = 0$, then
a mathematically convenient definition of $U(t)$ is
\begin{equation}
  \label{eq:definition-surplus}
  U(t) = u + c(t) - S(t).
\end{equation}
As mentioned previously, technical ruin of the insurance company
occurs when the surplus becomes negative. Therefore, the definition of
the infinite time probability of ruin is
\begin{equation}
  \label{eq:definition-ruin}
  \psi(u) = \Pr[U(t) < 0 \text{ for some } t \geq 0].
\end{equation}

We define some other quantities needed in the sequel. Let $N(t)$
denote the number of claims up to time $t \geq 0$ and $C_j$ denote the
amount of claim $j$. Then the definition of $S(t)$ is analogous to
\eqref{eq:definition-S}:
\begin{equation}
  \label{eq:definition-S(t)}
  S(t) = C_1 + \dots + C_{N(t)},
\end{equation}
assuming $N(0) = 0$ and $S(t) = 0$ as long as $N(t) = 0$. Furthermore,
let $T_j$ denote the time when claim $j$ occurs, such that $T_1 < T_2
< T_3 < \dots$ Then the random variable of the interarrival (or wait)
time between claim $j - 1$ and claim $j$ is defined as $W_1 = T_1$ and
\begin{equation}
  \label{eq:definition-wait}
  W_j = T_j - T_{j - 1}, \quad j \geq 2.
\end{equation}

For the rest of this discussion, we make the following assumptions:
\begin{enumerate}
\item premiums are collected at a constant rate $c$, hence $c(t) =
  ct$;
\item the sequence $\{T_j\}_{j \geq 1}$ forms an ordinary renewal
  process, with the consequence that  random variables $W_1, W_2,
  \dots$ are independent and identically distributed;
\item claim amounts $C_1, C_2, \dots$ are independent and identically
  distributed.
\end{enumerate}



\section{Adjustment coefficient}
\label{sec:adjustment-coefficient}

The quantity known as the adjustment coefficient $\rho$ hardly has any
physical interpretation, but it is useful as an approximation to
the probability of ruin since we have the inequality
\begin{displaymath}
  \psi(u) \leq e^{-\rho u}, \quad u \geq 0.
\end{displaymath}
The adjustment coefficient is defined as the smallest strictly
positive solution (if it exists) of the Lundberg equation
\begin{equation}
  \label{eq:definition-adjcoef}
  h(t) = \E{e^{t C - t c W}} = 1,
\end{equation}
where the premium rate $c$ satisfies the positive safety loading
constraint $\E{C - cW} < 0$. If $C$ and $W$ are independent, as in the
most common models, then the equation can be rewritten as
\begin{equation}
  \label{eq:definition-adjcoef-ind}
  h(t) = M_C(t) M_W(-tc) = 1.
\end{equation}

Function \code{adjCoef} of \pkg{actuar} computes the adjustment
coefficient $\rho$ from the following arguments: either the two moment
generating functions $M_C(t)$ and $M_W(t)$ (thereby assuming
independence) or else function $h(t)$; the premium rate $c$; the upper
bound of the support of $M_C(t)$ or any other upper bound for $\rho$.

For example, if $W$ and $C$ are independent, $W \sim
\text{Exponential}(2)$, $C \sim \text{Exponential}(1)$ and the premium
rate is $c = 2.4$ (for a safety loading of 20\% using the expected
value premium principle), then the adjustment coefficient is
<<echo=TRUE>>=
adjCoef(mgf.claim = mgfexp(x), mgf.wait = mgfexp(x, 2),
        premium.rate = 2.4, upper = 1)
@

The function also supports models with proportional or excess-of-loss
reinsurance \citep{Centeno_02}. Under the first type of treaty, an
insurer pays a proportion $\alpha$ of every loss and the rest is paid
by the reinsurer. Then, for fixed $\alpha$ the adjustment coefficient
is the solution of
\begin{equation}
  \label{eq:definition-adjcoef-prop}
  h(t) = \E{e^{t \alpha C - t c(\alpha) W}} = 1.
\end{equation}
Under an excess-of-loss treaty, the primary insurer pays each claim up
to a limit $L$. Again, for fixed $L$, the adjustment coefficient is
the solution of
\begin{equation}
  \label{eq:definition-adjcoef-xl}
  h(t) = \E{e^{t \min(C, L) - t c(L) W}} = 1.
\end{equation}

For models with reinsurance, \code{adjCoef} returns an object of
class \code{"adjCoef"} inheriting from the \code{"function"} class.
One can then use the object to compute the adjustment coefficient for
any retention rate $\alpha$ or retention limit $L$. The package also
defines a method of \code{plot} for these objects.

For example, using the same assumptions as above with proportional
reinsurance and a 30\% safety loading for the reinsurer, the
adjustment coefficient as a function of $\alpha \in [0, 1]$ is (see
Figure \ref{fig:adjcoef} for the graph):
<<echo=TRUE, fig=FALSE>>=
mgfx <- function(x, y) mgfexp(x * y)
p <- function(x) 2.6 * x - 0.2
rho <- adjCoef(mgfx, mgfexp(x, 2), premium = p, upper = 1, reins = "prop",
              from = 0, to = 1)
rho(c(0.75, 0.8, 0.9, 1))
plot(rho)
@

\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
plot(rho)
@
  \caption{Adjustment coefficient as a function of the retention rate}
  \label{fig:adjcoef}
\end{figure}


\section{Probability of ruin}
\label{sec:ruin}

In this subsection, we always assume that interarrival times and claim
amounts are independent.

The main difficulty with the calculation of the infinite time
probability of ruin lies in the lack of explicit formulas except for
the most simple models. If interarrival times are
Exponential$(\lambda)$ distributed (Poisson claim number process) and
claim amounts are Exponential$(\beta)$ distributed, then
\begin{equation}
  \label{eq:ruin-cramer-lundberg}
  \psi(u) = \frac{\lambda}{c \beta}\, e^{-(\beta - \lambda/c) u}.
\end{equation}
If the frequency assumption of this model is defensible, the severity
assumption can hardly be used beyond illustration purposes.

Fortunately, phase-type distributions have come to the rescue since
the early 1990s. \cite{AsmussenRolski_91} first show that in the
classical Cramér--Lundberg model where interarrival times are
Exponential$(\lambda)$ distributed, if claim amounts are
Phase-type$(\pmb{\pi}, \mat{T})$ distributed, then $\psi(u) = 1
- F(u)$, where $F$ is Phase-type$(\pmb{\pi}_+, \mat{Q})$ with
\begin{equation}
  \label{eq:prob-ruin:cramer-lundberg}
  \begin{split}
    \pmb{\pi}_+
    &= - \frac{\lambda}{c}\, \pmb{\pi} \mat{T}^{-1} \\
    \mat{Q}
    &= \mat{T} + \mat{t} \pmb{\pi}_+,
  \end{split}
\end{equation}
and $\mat{t} = -\mat{T} \mat{e}$, $\mat{e}$ is a column vector with
all components equal to 1; see the \code{"lossdist"} vignette for
details.

In the more general Sparre~Andersen model where interarrival times can
have any Phase-type$(\mat{\nu}, \mat{S})$ distribution,
\cite{AsmussenRolski_91} also show that using the same claim severity
assumption as above, one still has $\psi(u) = 1 - F(u)$ where $F$ is
Phase-type$(\pmb{\pi}_+, \mat{Q})$, but with parameters
\begin{equation}
  \label{eq:prob-ruin:sparre:pi+}
  \pmb{\pi}_+  = \frac{\mat{e}^\prime (\mat{Q} - \mat{T})}{%
    c \mat{e}^\prime \mat{t}}
\end{equation}
and $\mat{Q}$ solution of
\begin{equation}
  \label{eq:eq:prob-ruin:sparre:Q}
  \begin{split}
    \mat{Q}
    &= \Psi(\mat{Q}) \\
    &= \mat{T} - \mat{t} \pmb{\pi}
    \left[
      (\mat{I}_n \otimes \mat{\nu})
      (\mat{Q} \oplus \mat{S})^{-1}
      (\mat{I}_n \otimes \mat{s})
    \right].
  \end{split}
\end{equation}
In the above, $\mat{s} = -\mat{S} \mat{e}$, $\mat{I}_n$ is the $n
\times n$ identity matrix, $\otimes$ denotes the usual Kronecker
product between two matrices and $\oplus$ is the Kronecker sum defined
as
\begin{equation}
  \label{eq:kronecker-sum}
  \mat{A}_{m \times m} \oplus \mat{B}_{n \times n} =
  \mat{A} \otimes \mat{I}_n + \mat{B} \otimes \mat{I}_m.
\end{equation}

Function \code{ruin} of \pkg{actuar} returns a function object of
class \code{"ruin"} to compute the probability of ruin for any initial
surplus $u$. In all cases except the exponential/exponential model
where \eqref{eq:ruin-cramer-lundberg} is used, the output object calls
function \code{pphtype} to compute the ruin probabilities.

Some thought went into the interface of \code{ruin}. Obviously, all
models can be specified using phase-type distributions, but the
authors wanted users to have easy access to the most common models
involving exponential and Erlang distributions. Hence, one first
states the claim amount and interarrival times models with any
combination of \code{"exponential"}, \code{"Erlang"} and
\code{"phase-type"}. Then, one passes the parameters of each model
using lists with components named after the corresponding parameters
of \code{dexp}, \code{dgamma} and \code{dphtype}. If a component
\code{"weights"} is found in a list, the model is a mixture of
exponential or Erlang (mixtures of phase-type are not supported).
Every component of the parameter lists is recycled as needed.

The following examples should make the matter clearer. (All examples
use $c = 1$, the default value in \code{ruin}.) First, for the
exponential/exponential model, one has
<<echo=TRUE>>=
psi <- ruin(claims = "e", par.claims = list(rate = 5),
            wait   = "e", par.wait   = list(rate = 3))
psi
psi(0:10)
@

Second, for a mixture of two exponentials claim amount model and
exponential interarrival times, the simplest call to \code{ruin} is
<<echo=TRUE>>=
ruin(claims = "e", par.claims = list(rate = c(3, 7), weights = 0.5),
     wait   = "e", par.wait   = list(rate = 3))
@

Finally, one will obtain a function to compute ruin probabilities in a
model with phase-type claim amounts and mixture of exponentials
interarrival times with
<<echo=TRUE>>=
prob <- c(0.5614, 0.4386)
rates <- matrix(c(-8.64, 0.101, 1.997, -1.095), 2, 2)
ruin(claims = "p", par.claims = list(prob = prob, rates = rates),
     wait   = "e", par.wait   = list(rate = c(5, 1), weights = c(0.4, 0.6)))
@

To ease plotting of the probability of ruin function, the package
provides a method of \code{plot} for objects returned by \code{ruin}
that is a simple wrapper for \code{curve} (see Figure
\ref{fig:prob-ruin}):
<<echo=TRUE, fig=FALSE>>=
psi <- ruin(claims = "p", par.claims = list(prob = prob, rates = rates),
     wait   = "e", par.wait   = list(rate = c(5, 1), weights = c(0.4, 0.6)))
plot(psi, from = 0, to = 50)
@

\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
plot(psi, from = 0, to = 50)
@
  \caption{Graphic of the probability of ruin as a function of the
    initial surplus $u$}
  \label{fig:prob-ruin}
\end{figure}


\section{Approximation to the probability of ruin}
\label{sec:beekman}

When the model for the aggregate claim process
\eqref{eq:definition-S(t)} does not fit nicely into the framework of
the previous section, one can compute ruin probabilities using the
so-called Beekman's convolution formula
\citep{Beekman_68,BeekmanFormula_EAS}.

Let the surplus process and the aggregate claim amount process be
defined as in \eqref{eq:definition-surplus} and
\eqref{eq:definition-S(t)}, respectively, and let $\{N(t)\}$ be a
Poisson process with mean $\lambda$. As before, claim amounts $C_1,
C_2, \dots$ are independent and identically distributed with cdf
$P(\cdot)$ and mean $\mu = \E{C_1}$. Then the infinite time
probability of ruin is given by
\begin{equation}
  \label{eq:beekman:prob-ruin}
  \psi(u) = 1 - F(u),
\end{equation}
where $F(\cdot)$ is Compound~Geometric$(p, H)$ with
\begin{equation}
  \label{eq:beekman:p}
  p = 1 - \frac{\lambda \mu}{c}
\end{equation}
and
\begin{equation}
  \label{eq:beekman:H}
  H(x) = \int_0^x \frac{1 - P(y)}{\mu}\, dy.
\end{equation}
In other words, we have (compare with \eqref{eq:cdf-S})
\begin{equation}
  \label{eq:beekman:prob-ruin-long}
  \psi(u) = 1 - \sum_{n = 0}^\infty  H^{*n}(u) p (1 - p)^n.
\end{equation}

In most practical situations, numerical evaluation of
\eqref{eq:beekman:prob-ruin-long} is done using Panjer's recursive
formula. This usually requires discretization of $H(\cdot)$. In such
circumstances, Beekman's formula yields approximate ruin
probabilities.

For example, let claim amounts have a Pareto$(5, 4)$ distribution,
that is
\begin{displaymath}
  P(x) = 1 - \left( \frac{4}{4 + x} \right)^5
\end{displaymath}
and $\mu = 1$. Then
\begin{align*}
  H(x)
  &= \int_0^x \left( \frac{4}{4 + y} \right)^5 dy \\
  &= 1 - \left( \frac{4}{4 + x} \right)^4,
\end{align*}
or else $H$ is Pareto$(4, 4)$. Furthermore, we determine the premium
rate $c$ with the expected value premium principle and a safety
loading of 20\%, that is $c = 1.2 \lambda \mu$. Thus, $p = 0.2/1.2 =
1/6$.

One can get functions to compute lower bounds and upper bounds for
$F(u)$ with functions \code{discretize} and \code{aggregateDist}
as follows:
<<echo=TRUE>>=
f.L <- discretize(ppareto(x, 4, 4), from = 0, to = 200, step = 1, method = "lower")
f.U <- discretize(ppareto(x, 4, 4), from = 0, to = 200, step = 1, method = "upper")
F.L <- aggregateDist(method = "recursive", model.freq = "geometric",
                     model.sev = f.L, prob = 1/6)
F.U <- aggregateDist(method = "recursive", model.freq = "geometric",
                     model.sev = f.U, prob = 1/6)
@

Corresponding functions for the probability of ruin $\psi(u)$ lower
and upper bounds are (see figure \ref{fig:beekman:prob-ruin} for the
graphic):
<<echo=TRUE>>=
psi.L <- function(u) 1 - F.U(u)
psi.U <- function(u) 1 - F.L(u)
u <- seq(0, 50, by = 5)
cbind(lower = psi.L(u), upper = psi.U(u))
curve(psi.L, from = 0, to = 100, col = "blue")
curve(psi.U, add = TRUE, col = "green")
@

\begin{figure}[t]
  \centering
<<echo=FALSE, fig=TRUE>>=
curve(psi.L, from = 0, to = 100, col = "blue")
curve(psi.U, add = TRUE, col = "green")
@
  \caption{Lower and upper bounds for the probability of ruin as
    determined using Beekman's convolution formula.}
  \label{fig:beekman:prob-ruin}
\end{figure}

One can make the bounds as close as one wishes by reducing the
discretization step.

\bibliography{actuar}

\end{document}

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