An Introduction to Stochastic Processes by Edward P. C.(Edward P.C. Kao) Kao

By Edward P. C.(Edward P.C. Kao) Kao

Meant for a calculus-based path in stochastic techniques on the graduate or complicated undergraduate point, this article deals a latest, utilized standpoint. rather than the traditional formal and mathematically rigorous process traditional for texts for this direction, Edward Kao emphasizes the advance of operational abilities and research via quite a few well-chosen examples.

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1). So we have, with g(t) = E sups≤t |X(x, s) − X(y, s)|2p , that t g(t) ≤ c3 |x − y|2p + c4 g(s) ds, t ≤ t0 . 0 By Gronwall’s lemma, E sup |X(x, s) − X(y, s)|2p ≤ c5 |x − y|2p . s. 11]. The same proof shows that one is not required to have the index set be [0, ∞). If E |Yx − Yy |p ≤ c7 |x − y|d+ε for x, y ∈ Rd , then {Yx , x ∈ D} is uniformly continuous, where here D is the collection of points in Rd all of whose coordinates are dyadic rationals. The proof also shows that we may replace | · | by any metric or norm.

Proof. Let Bt = inf{u : M u > t}. Then Wt = MBt is a continuous martingale with quadratic variation equal to t; hence by L´evy’s theorem (Section 1), t Wt is a Brownian motion. If Zt = YBt = Wt + Et , then Et = 0 es ds for some es bounded by c4 , where c4 depends only on c1 and c2 . Our assertion will follow if we can show P( sup |Zs | < ε) ≥ c3 . s≤c1 t0 We now use Girsanov’s theorem. Define a probability measure Q by dQ/dP = exp t0 − 0 es dWs − 1 2 t0 e2s ds 0 on Ft0 . Under P, Wt is a martingale, so under Q we have that 8.

0 Since Lu = 0 inside D, taking expectations shows u(x) = E x u(Xt∧Sn ). We let t → ∞ and then n → ∞. By dominated convergence, we obtain u(x) = E x u(XτD ). This is what we want since u = f on ∂D. 1. One is the maximum principle: if x ∈ D, sup u ≤ sup u. 2) ∂D This follows from u(x) = E x f (XτD ) ≤ sup f. 1. 2) Proposition. Let f be continuous on ∂D and suppose v(x) = E x f (XτD ) is continuous on D and C 2 on D. Suppose the coefficients of L are continuous. Then Lv = 0 on D. Proof. By the strong Markov property at time τB(x,r) , the time to exit B(x, r), we have v(x) = E x v(Xτ (B(x,r)) ) if r is small enough that B(x, r) ⊆ D.

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