Let (X(t))t⩾0 be a continuous-time Markov process with state space I={1,…,n} and generator Q=(qij)i,j∈I satisfying qij=qji for all i,j∈I. The local time up to time t>0 of X is the random vector L(t)=(Li(t))i∈I∈Rn defined by
Li(t)=∫0t1X(s)=ids(i∈I)
(a) Let f:I×Rn→R be any function that is differentiable with respect to its second argument, and set
ft(i,ℓ)=Eif(X(t),ℓ+L(t)),(i∈I,ℓ∈Rn)
Show that
∂t∂ft(i,ℓ)=Mft(i,ℓ)
where
Mf(i,ℓ)=j∈I∑qijf(j,ℓ)+∂ℓi∂f(i,ℓ)
(b) For y∈Rn, write y2=(yi2)i∈I∈[0,∞)n for the vector of squares of the components of y. Let f:I×Rn→R be a function such that f(i,ℓ)=0 whenever ∑j∣ℓj∣⩾T for some fixed T. Using integration by parts, or otherwise, show that for all i
−∫Rnexp(21yTQy)yij=1∑nyjMf(j,21y2)dy=∫Rnexp(21yTQy)f(i,21y2)dy,
where yTQy denotes ∑k,m∈Iykqkmym.
(c) Let g:Rn→R be a function with g(ℓ)=0 whenever ∑j∣ℓj∣⩾T for some fixed T. Given t>0,j∈I, now let
f(i,ℓ)=Ei[g(ℓ+L(t))1X(t)=j]
in part (b) and deduce, using part (a), that
∫Rnexp(21yTQy)yiyjg(21y2)dy=∫Rnexp(21yTQy)(∫0∞Ei[1X(t)=jg(21y2+L(t))]dt)dy
[You may exchange the order of integrals and derivatives without justification.]