March 6th, 2002, 12:56 pm
Here is what I showed Reza earlier: Let X_t=ln(S_t), then X_t = (r-sigma^2/2)t+ sigma B_t, X_0=x <ln(H) let us also denote mu=r-sigma^2. X_t=mu* t+ sigma B_t. In case of mu=0, E(tau)=\infty, that's true( a classical martingale excercise) If not, X_min(tau,t) is a martingale, since [X, X]_min(tau,t)= min(tau,t) and E(tau) finite means that using monotone convergence theorem ln(H)=E(X_tau)=E(X_0)=x which is a contradiction. Similarly that we get the case for positive mu where E(tau) is finite. But I think when mu is negative, E(tau) does not exist, since otherwise we would have X_min{tau,t}-mu*min(tau,t) is a martingale, using monotone convergence theorem we get E(X_tau)-mu*E(tau)=x, i.e, ln(H)-mu*E(tau)=x which is a contradiction, since E(tau)>0.....I might be wrong.