%\VignetteIndexEntry{Complement to Gatto, R. and Baumgartner, B. (2016)} \documentclass{article} \usepackage[noae]{Sweave} \usepackage[paper=a4paper]{geometry} \usepackage[T1]{fontenc} \usepackage{fourier} \usepackage[english]{babel} \usepackage[utf8]{inputenc} \usepackage[fleqn]{amsmath} \usepackage{amsthm, amssymb} \usepackage{graphicx} \title{% Saddlepoint approximations to the probability of ruin in finite time for the compound Poisson risk process perturbed by diffusion (Complement)% } \date{2014} \author{Riccardo Gatto \and Benjamin Baumgartner} \newcommand*{\1}{\mathbb{1}} \newcommand*{\E}{\mathbb{E}} \newcommand*{\R}{\mathbb{R}} \makeatletter \newcommand*{\diffstar}{\mathrm{d}} \newcommand*{\diffnostar}{\unskip{\;\mathrm{d}}} \newcommand*{\diff}{\@ifstar{\diffstar}{\diffnostar}} \makeatother \newcommand*{\e}{\mathrm{e}} \newcommand*{\oh}{\mathrm{o}} \newcommand*{\shift}{\ensuremath{\mathrel{\phantom{=}}\strut}} \makeatletter \renewenvironment{proof}[1][\proofname]{\par\pushQED{\qed}\normalfont\topsep6\p@\@plus6\p@\relax\trivlist\item[\hskip\labelsep\bfseries#1\@addpunct{.}]\ignorespaces}{\popQED\endtrivlist\@endpefalse} \makeatother \begin{document} \maketitle \section*{Introduction} This document serves as a complement to \begin{quote} [GB]\quad Gatto, R.\ and Baumgartner, B.\ (2016). ``Saddlepoint approximations to the probability of ruin in finite time for the compound Poisson risk process perturbed by diffusion''. \emph{Methodology and Computing in Applied Probability} \textbf{18}(1), pp.\ 217--235. \end{quote} The joint or double Laplace transform of the time of ruin $T$ and the initial capital $Y_{0}$ is given in GB (Theorem~2.1). A proof using the Laplace transform of the Gerber--Shiu function is given in GB (Appendix) and a complete proof, which does not require the Gerber--Shiu function, is the following. \section*{Alternative proof of Theorem~2.1} \begin{proof}[Proof of Theorem~2.1] Within any small time interval of length $h > 0$, either zero, one or more than one jumps (or claims) occur with the corresponding probabilities $1 - \lambda h + \oh(h)$, $\lambda h + \oh(h)$ and $\oh(h)$, as $h \downarrow 0$. Thus, from the independence and the stationarity of the increments of $\{Y_{t}\}_{t \ge 0}$, we obtain \begin{align} f_{\alpha}(x) &= (1 - \lambda h)\,\E\E\bigl[\e^{\alpha(T + h)} \1(T < \infty) \bigm| Y_{0} = x + ch + \sigma W_{h}\bigr] \nonumber \\ &\shift + \lambda h\,\E\E\bigl[\e^{\alpha(T + h)} \1(T < \infty) \bigm| Y_{0} = x + ch + \sigma W_{h} - X_{1}\bigr] + \oh(h) \nonumber \\ &= (1 - \lambda h)\,\e^{\alpha h}\,\E\bigl[f_{\alpha}\bigl(x + ch + \sigma W_{h}\bigr)\bigr] + \lambda h\,\e^{\alpha h}\,\E\bigl[f_{\alpha}\bigl(x + ch + \sigma W_{h} - X_{1}\bigr)\bigr] + \oh(h), \label{eqn:doubletransf-expsplit-as-f} \end{align} as $h \downarrow 0$ and for any $\alpha \in \R$ such that $f_\alpha(x) < \infty$. The first expectation in \eqref{eqn:doubletransf-expsplit-as-f} can be written as \begin{align} \E\bigl[f_{\alpha}\bigl(x + ch + \sigma W_{h}\bigr)\bigr] &= f_{\alpha}(x) + \E\left[\sum_{k=1}^{2} \frac{f_{\alpha}^{(k)}(x)}{k!}\bigl(ch + \sigma W_{h}\bigr)^{k} + \oh \left( \{ c h + \sigma W_h \}^2 \right) \right] \nonumber\\ &= f_{\alpha}(x) + \sum_{k=1}^{2} \left(\frac{f_{\alpha}^{(k)}(x)}{k!} \sum_{i=0}^{k} \binom{k}{i} (ch)^{k-i}\;\sigma^{i}\,\E\bigl[W_{h}^{i}\bigr]\right) + \E\left[ \oh \left( W_h^2 \right) \right] \nonumber\\ &= f_{\alpha}(x) + ch f'_{\alpha}(x) + \tfrac{1}{2}\sigma^{2}h f''_{\alpha}(x) + \oh(h). \label{eqn:doubletransf-exp-taylor-one} \end{align} Similarly, the second expectation in \eqref{eqn:doubletransf-expsplit-as-f} can be written as \begin{align} \E\bigl[f_{\alpha}\bigl(x + ch + \sigma W_{h} - X_{1}\bigr)\bigr] &= \E\bigl[f_\alpha(x - X_{1})\bigr] + \E\left[\sum_{k=1}^{2}\frac{f_{\alpha}^{(k)}(x - X_{1})}{k!} \bigl(ch + \sigma W_{h}\bigr)^{k} + \oh \left( \{ c h + \sigma W_h \}^2 \right) \right] \nonumber\\ &= \E\bigl[f_\alpha(x - X_{1})\bigr] + \sum_{k=1}^{2} \left(\frac{\E\bigl[f_{\alpha}^{(k)}(x - X_{1})\bigr]}{k!} \sum_{i=0}^{k} \binom{k}{i} (ch)^{k-i}\;\sigma^{i}\,\E\bigl[W_{h}^{i}\bigr]\right) + \E\left[ \oh \left( W_h^2 \right) \right] \nonumber\\ &= \E\bigl[f_{\alpha}(x - X_{1})\bigr] + ch\,\E\bigl[f_{\alpha}'(x - X_{1})\bigr] + \tfrac{1}{2}\sigma^{2}h\,\E\bigl[f_{\alpha}''(x - X_{1})\bigr] + \oh(h). \label{eqn:doubletransf-exp-taylor-two} \end{align} As indicated by a Referee, the existence of $f_{\alpha}''$, required in the Taylor expansions in \eqref{eqn:doubletransf-exp-taylor-one} and \eqref{eqn:doubletransf-exp-taylor-two}, is established by Feng (2011, Lemma C.1), because the function $f_{\alpha}$ is a special case of the more general functional of $T$ given in GB (Equation 15). Replacing the expectations in \eqref{eqn:doubletransf-expsplit-as-f} with their respective expansions~\eqref{eqn:doubletransf-exp-taylor-one} and \eqref{eqn:doubletransf-exp-taylor-two}, dividing both sides by $\e^{\alpha h} h (1 - \lambda h)$ and rearranging terms results in \begin{align*} 0 &= \frac{1}{h}\Biggr(1- \frac{\e^{-\alpha h}}{1 - \lambda h}\Biggr) f_{\alpha}(x) + cf_{\alpha}'(x) + \tfrac{1}{2}\sigma^{2} f_{\alpha}''(x) \\ &\shift + \frac{1}{1 - \lambda h}\biggl(\E\bigl[f_{\alpha}(x - X_{1})\bigr] + ch\,\E\bigl[f_{\alpha}'(x - X_{1})\bigr] + \tfrac{1}{2}\sigma^{2}h\,\E\bigl[f_{\alpha}''(x - X_{1})\bigr]\biggr) + \oh(1). \end{align*} Let $g(h) = \e^{-\alpha h}(1 - \lambda h)^{-1}$, then by the rule of de l'Hospital the coefficient of $f_{\alpha}(x)$ converges to $\lim_{h \downarrow 0} \bigl(g(0) - g(h)\bigr) / h = -g'(0) = \alpha - \lambda$. Thus, by letting $h \downarrow 0$, we obtain the integro-differential equation \begin{align} 0 &= \tfrac{1}{2}\sigma^{2} f''_{\alpha}(x) + cf'_{\alpha}(x) + (\alpha-\lambda)f_{\alpha}(x) + \lambda\,\E\bigl[f_{\alpha}(x - X_{1})\bigr] \nonumber \\ \intertext{or, equivalently,} 0 &= \tfrac{1}{2}\sigma^{2} f''_{\alpha}(x) + cf'_{\alpha}(x) + (\alpha-\lambda)f_{\alpha}(x) + \lambda \int_{0}^{x} f_{\alpha}(x-\xi)\diff F_{X}(\xi) + \lambda[1 - F_{X}(x)]. \label{eqn:intdiffeq} \end{align} In the next step, both sides of \eqref{eqn:intdiffeq} are multiplied by $\e^{\beta x}$ and integrated from $0$ to $\infty$. This corresponds to taking Laplace transforms with reversed sign of the argument $\beta$, thus $\smash{\widehat{f_{\alpha}'}}(\beta) = -\beta \hat{f}_{\alpha}(\beta) - f_{\alpha}(0)$ and $\smash{\widehat{f_{\alpha}''}}(\beta) = \beta^{2} \hat{f}_{\alpha}(\beta) + \beta f_{\alpha}(0) - f_{\alpha}'(0)$, for any $\beta \in \R$ such that $\hat{f}_\alpha(\beta) < \infty$, where $\hat{g}(u) = \int_0^\infty \e^{u x} g(x) \diff x$, for a generic function $g$. As a consequence, \begin{align*} 0 &= \tfrac{1}{2}\sigma^{2} \bigl[\beta^{2}\hat{f}_{\alpha}(\beta) + \beta f_{\alpha}(0) - f'_{\alpha}(0)\bigr] + c\bigl[-\beta \hat{f}_{\alpha}(\beta) - f_{\alpha}(0)\bigr] \\ &\shift + (\alpha - \lambda) \hat{f}_{\alpha}(\beta) + \lambda \hat{f}_{\alpha}(\beta) M_{X}(\beta) - \tfrac{\lambda}{\beta} \bigl[1 - M_{X}(\beta)\bigr], \end{align*} which, when solved for $\hat{f}_{\alpha}(\beta)$, which is the left-hand side of \eqref{eqn:double-laplace-transform}, leads to \begin{align*} \hat{f}_{\alpha}(\beta) &= \frac{% \bigl(c - \frac{1}{2}\sigma^{2}\beta\bigr) f_{\alpha}(0) + \tfrac{1}{2}\sigma^{2} f'_{\alpha}(0) - \tfrac{\lambda}{\beta} \bigl(M_{X}(\beta) - 1\bigr) }{% \tfrac{1}{2}\sigma^{2}\beta - c\beta + \alpha + \lambda \bigl(M_{X}(\beta) - 1\bigr) }\\ &= \frac{% \bigl(c - \tfrac{1}{2}\sigma^{2}\beta\bigr) f_{\alpha}(0) + \tfrac{1}{2}\sigma^{2} f'_{\alpha}(0) - \frac{\kappa(\beta)}{\beta} + \tfrac{1}{2}\sigma^{2}\beta - c }{% \kappa(\beta) + \alpha }. \end{align*} Note that $\hat{f}_{\alpha}(\beta)$ exists for all $\beta < 0$, if $\alpha < 0$. In particular, it exists for $\beta = v(\alpha) < 0$ with $\alpha < 0$. In this case the above denominator vanishes and therefore $v(\alpha)$ is a common root of both the denominator and numerator above, otherwise the existence of $\hat{f}_{\alpha}(v(\alpha))$ would be contradicted. Because of that, setting the numerator equal to $0$ and substituting $v(\alpha)$ for $\beta$ yields \begin{equation*} \tfrac{1}{2}\sigma^{2} f'_{\alpha}(0) = - \bigl(c - \tfrac{1}{2}\sigma^{2} v(\alpha)\bigr)\;f_{\alpha}(0) - \frac{\alpha}{v(\alpha)} - \tfrac{1}{2}\sigma^{2} v(\alpha) + c, \end{equation*} and hence \begin{equation*} \hat{f}_{\alpha}(\beta) = \frac{% \frac{1}{2}\sigma^{2} \bigl(\beta - v(\alpha)\bigr) \bigl(1 - f_{\alpha}(0)\bigr) - \frac{\kappa(\beta)}{\beta} - \frac{\alpha}{v(\alpha)} }{% \kappa(\beta) + \alpha }. \end{equation*} Without initial reserve, i.\,e.\ for $x = 0$, ruin occurs almost surely and $T = 0$ a.\,s.\ because the regularity of the Wiener process implies that the risk process crosses the null level infinitely often over any arbitrarily small time interval containing the origin. Hence $f_{\alpha}(0) = 1$ and, as a consequence, GB (Equation 12), i.\,e. \begin{equation} \hat{f}_{\alpha}(\beta) = -\frac{% \frac{\alpha}{v(\alpha)} + \frac{\kappa(\beta)}{\beta} }{% \alpha + \kappa(\beta) } \label{eqn:double-laplace-transform} \end{equation} holds for all $\alpha, \beta < 0$. From the fact that $D = \{(\alpha, \beta) \in \R^{2}\colon \alpha \le \mathring{\alpha},\ \beta < \tilde{v}(\alpha)\}$ is a connected subset of $\R^{2}$ and the right-hand side of \eqref{eqn:double-laplace-transform} is an analytical function for all $\alpha, \beta \in D$, follows that the double Laplace transform formula~\eqref{eqn:double-laplace-transform} holds over the entire set $D$. \end{proof} A helpful picture of the domain $D$ is given by Figure \ref{f1}. \begin{figure}[htbp] \centering \includegraphics[width=0.7\linewidth]{gb2016_complement_domain_fig.pdf} \caption{A representation of the domain $D$ of the double Laplace transform $\hat{f}_{\alpha}(\beta)$} \label{f1} \end{figure} \end{document}