  We analyse the optimal exercise of an executive stock option (ESO) written on
a stock whose drift parameter falls to a lower value at a change point, an
exponentially distributed random time independent of the Brownian motion
driving the stock. Two agents, who do not trade the stock, have differing
information on the change point, and seek to optimally exercise the option by
maximising its discounted payoff under the physical measure. The first agent
has full information, and observes the change point. The second agent has
partial information and filters the change point from price observations. This
scenario is designed to mimic the positions of two employees of varying
seniority, a fully informed executive and a partially informed less senior
employee, each of whom receives an ESO. The partial information scenario yields
a model under the observation filtration $\widehat{\mathbb{F}}$ in which the
stock drift becomes a diffusion driven by the innovations process, an
$\widehat{\mathbb{F}}$-Brownian motion also driving the stock under
$\widehat{\mathbb{F}}$, and the partial information optimal stopping value
function has two spatial dimensions. We rigorously characterise the free
boundary PDEs for both agents, establish shape and regularity properties of the
associated optimal exercise boundaries, and prove the smooth pasting property
in both information scenarios, exploiting some stochastic flow ideas to do so
in the partial information case. We develop finite difference algorithms to
numerically solve both agents' exercise and valuation problems and illustrate
that the additional information of the fully informed agent can result in
exercise patterns which exploit the information on the change point, lending
credence to empirical studies which suggest that privileged information of bad
news is a factor leading to early exercise of ESOs prior to poor stock price
performance.
