Homework Problems - Stochastic Processes | MATH 285, Assignments of Stochastic Processes

Material Type: Assignment; Class: Stochastic Processes; Subject: Mathematics; University: University of California - San Diego; Term: Spring 2009;

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Pre 2010

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Math 285, Spring 2009
Homework 10, not to be handed in
1. Exercise 9.5, page 229 of the text.
2. Exercise 9.6, page 230 of the text.
3. Let Wt,t0, be standard one-dimensional Brownian motion, with W0= 0. Define, for t0
and k= 0,1,2,...,
mk(t) = E[W2k
t].
Use Itˆo’s formula (and then take expectations—the martingale terms have mean zero!) to show
that
mk(t) = t(2k1)mk1(t), t > 0, k = 1,2,3,....
Deduce by recursion that
mk(t) = 1 ·3·5···(2k1)tk, t > 0,k = 1,2,....
4. Let Wt,t0, be standard one-dimensional Brownian motion, with W0= 0. Let fand gbe
(non-random) continuous functions on [0,1] and suppose there are constants Cand Dsuch that
C+Z1
0
f(s)dWs=D+Z1
0
g(s)dWs.
Show that C=Dand that f(s) = g(s) for all s[0,1].
[Hints: The martingale Mt=Rt
0[f(s)g(s)] dWssatisfies M0= 0 and M1=DC. Deduce from
this that C=D. Consequently, M1= 0. Now use the formula for the variance of a stochastic
integral to see that R1
0[f(s)g(s)]2ds = 0.]
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Math 285, Spring 2009 Homework 10, not to be handed in

  1. Exercise 9.5, page 229 of the text.
  2. Exercise 9.6, page 230 of the text.
  3. Let Wt, t ≥ 0, be standard one-dimensional Brownian motion, with W 0 = 0. Define, for t ≥ 0 and k = 0, 1 , 2 ,.. ., mk(t) = E[W (^) t^2 k].

Use Itˆo’s formula (and then take expectations—the martingale terms have mean zero!) to show that mk (t) = t(2k − 1)mk− 1 (t), t > 0 , k = 1, 2 , 3 ,....

Deduce by recursion that

mk (t) = 1 · 3 · 5 · · · (2k − 1)tk, t > 0 , k = 1, 2 ,....

  1. Let Wt, t ≥ 0, be standard one-dimensional Brownian motion, with W 0 = 0. Let f and g be (non-random) continuous functions on [0, 1] and suppose there are constants C and D such that

C +

0

f (s) dWs = D +

0

g(s) dWs.

Show that C = D and that f (s) = g(s) for all s ∈ [0, 1]. [Hints: The martingale Mt =

∫ (^) t 0 [f^ (s)^ −^ g(s)]^ dWs^ satisfies^ M^0 = 0 and^ M^1 =^ D^ −^ C. Deduce from this that C = D. Consequently, M 1 = 0. Now use the formula for the variance of a stochastic integral to see that

0 [f^ (s)^ −^ g(s)]

(^2) ds = 0.]