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The inverse first passage time problem asks whether, for a Brownian motion$B$ and a nonnegative random variable $\zeta$, there exists a time-varyingbarrier $b$ such that $\mathbb{P}\{B_s>b(s),0\leq s\leqt\}=\mathbb{P}\{\zeta>t\}$. We study a "smoothed" version of this problem andask whether there is a "barrier" $b$ such that $\mathbb{E}[\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]=\mathbb{P}\{\zeta >t\}$,where $\lambda$ is a killing rate parameter, and $\psi:\mathbb{R}\to[0,1]$ is anonincreasing function. We prove that if $\psi$ is suitably smooth, thefunction $t\mapsto \mathbb{P}\{\zeta>t\}$ is twice continuously differentiable,and the condition $0<-\frac{d\log\mathbb{P}\{\zeta>t\}}{dt}<\lambda$ holds forthe hazard rate of $\zeta$, then there exists a unique continuouslydifferentiable function $b$ solving the smoothed problem. We show how thisresult leads to flexible models of default for which it is possible to computeexpected values of contingent claims.

Killed Brownian motion with a prescribed lifetime distribution and models of default