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If $X_t = \mu t + \sigma W_t$ with $W_t$ a Wiener process, I would like to know if the distribution for the last time $X_t = a$ is known - and if so, what it is. My googling has turned up a bunch of results for first exit time, first hitting time etc but these are not too useful to me. Any references/derivations are also highly appreciated, but I am a physicist without much training in probability, unfortunately.
Thanks in advance.
Yes you can compute the distribution of the last hitting time.
Assume $\mu,a>0$ so the last hitting time is a.s. finite. Basically let $B_t = tW_{1/t}$. which is also a brownian motion. This time inversion allows us to "convert" the last hitting time into a first hitting time.
Specifically, if $t_a = \sup\{t \ge 0 : \mu t + \sigma W_t \le a\}$ then $t_a^{-1} = \inf\{u \ge 0: au-\sigma B_u \ge \mu\}$. And the latter is something whose distribution you know how to compute, because it is a first hitting time.
$\def\si{\sigma}$ $\def\cF{\mathcal{F}}$ $\def\ol{\overline}$
Here is an alternative derivation. Let $B_t=(\mu/\si)W_{\si^2t/\mu^2}$, $Y_t=t+B_t$, and $b=a\mu/\si^2$. Then $X_t=a$ if and only if $Y_{\mu^2t/\si^2}=b$. Hence, if $T$ is the last time $X$ hits $a$ and $\tau$ is the last time $Y$ hits $b$, then $T=\si^2\tau/\mu^2$.
By the Markov property, \begin{align} P(\tau < t) &= P(Y_t > b, Y_s > b \text{ for all $s\ge t$})\\ &= E[P(Y_t > b, Y_s > b \text{ for all $s\ge t$} \mid \cF_t)]\\ &= E[1_{\{Y_t > b\}}P^{Y_t}(\tau_b = \infty)], \end{align} where $\tau_b$ is the first hitting time of $b$. For $y>b$, $$ P^y(\tau_b = \infty) = P(\tau_{b-y} = \infty) = 1 - e^{-2(y-b)}. $$ Thus, \begin{align} P(\tau < t) &= P(Y_t > b) - \frac1{\sqrt{2\pi t}}\int_b^\infty\exp\left({ -2(y - b) - \frac{(y - t)^2}{2t} }\right)\,dy\\ &= P(Y_t > b) - \frac1{\sqrt{2\pi t}}\int_b^\infty\exp\left({ 2b - \frac{(y + t)^2}{2t} }\right)\,dy\\ &= P(t + B_t > b) - e^{2b}P(-t + B_t > b)\\ &= \ol\Phi\left({\frac{b-t}{\sqrt t}}\right) - e^{2b}\ol\Phi\left({\frac{b+t}{\sqrt t}}\right), \end{align} where $\ol\Phi=1-\Phi$, and $\Phi$ is the standard normal distribution function. Differentiating and doing a little algebra gives the density of $\tau$, $$ f_\tau(t) = \frac1{\sqrt{2\pi t}}\exp\left({-\frac{(b-t)^2}{2t}}\right), $$ and from here, we get the density of $T$, $$ f_T(t) = \frac{\mu}{\si\sqrt{2\pi t}}\exp\left({ -\frac{(\si^2b-\mu^2t)^2}{2\mu^2\si^2t} }\right). $$ Not that you needed another derivation to tell you this, but there must be something wrong with your numerical simulations.
