Paper detail

Approximating the Laplace transform of the sum of dependent lognormals

Let $(X_1, \dots, X_n)$ be multivariate normal, with mean vector $\boldsymbolμ$ and covariance matrix $\boldsymbolΣ$, and $S_n=\mathrm{e}^{X_1}+\cdots+\mathrm{e}^{X_n}$. The Laplace transform ${\cal L}(θ)=\mathbb{E}\mathrm{e}^{-θS_n} \propto \int \exp\{-h_θ(\boldsymbol{x})\} \,\mathrm{d} \boldsymbol{x}$ is represented as $\tilde{\cal L}(θ)I(θ)$, where $\tilde{\cal L}(θ)$ is given in closed-form and $I(θ)$ is the error factor ($\approx 1$). We obtain $\tilde{\cal L}(θ)$ by replacing $h_θ(\boldsymbol{x})$ with a second order Taylor expansion around its minimiser $\boldsymbol{x}^*$. An algorithm for calculating the asymptotic expansion of $\boldsymbol{x}^*$ is presented, and it is shown that $I(θ)\to 1$ as $θ\to\infty$. A variety of numerical methods for evaluating $I(θ)$ are discussed, including Monte Carlo with importance sampling and quasi-Monte Carlo. Numerical examples (including Laplace transform inversion for the density of $S_n$) are also given.