1. Let Wt denote a standard Brownian motion. Evaluate the following expectationE[|Wt+t Wt|],
where | · | denote absolute value.
V [(Wt -Ws)2].
2. Let Wt denote a Brownian motion. Derive the stochastic dierential equation for dXt and
group the drift and diusion coecients together for the following stochastic processes:
(a) Xt = Wt2
(b) Xt =t+eWt
(c) Xt = Wt3 3tWt(d) Xt = et+Wt
(e) Xt = e2t sin(Wt)
(f) Xt =eWt2t
We need at least 9 more requests to produce the answer.
1 / 10 have requested this problem solution
The more requests, the faster the answer.
Let Wt denote a standard Brownian motion. Evaluate the following expectation
In the following questions, let Bt denote a Brownian motion with
B0 = 0.
Let Xt be the solution of SDE dX, = 3X, dt + 2XtdBt and Xo = 1. (a) Write down the SDE for Yt-eatXt, where a is a constant. (b) Find the value of a such that Yt is a martingale, and give the mean and variance of Y, in this case.
Let Xt be the solution of SDE dX, = 3X, dt + 2XtdBt and...
We consider a Standard Brownian Motion W={Wt,t>=o}, show that
for s<t, Ws|Wt=x the conditional distribution of the process
given a future valueWt=x
We consider a standard Brownian motion W W,t20) Show that for s < t, W /Wt-x the conditional distribution of the process given a future value Wi is given by the following Normal distribution:
Let x be an arithmetic brownian motion starting from 0 with
drift parameter 0.2
Let X-(Xt ,0 < t < 1} be an arithmetic Brownian motion starting from 0 with drift parameter μ-0.2 and variance parameter ơ2-0.125. 1. Calculate the probability that X2 is between 0.1 and 0.5 2. Given that X 0.6, find the probability that X2 is between 0.1 and 0.5 3. Given that Xi- 0.2, find the covariance between X2 and X3
let {X(t), 1 2 0} denote a Brownian motion 8.1. Let Y(t) = tx(1/t). (a) What is the distribution of Y(t)? (b) Compute Cov(Y(s), Y()) (c) Argue that {Y(t), t 2 0] is also Brownian motion (d) Let Using (c) present an argument that
let {X(t), 1 2 0} denote a Brownian motion
8.1. Let Y(t) = tx(1/t). (a) What is the distribution of Y(t)? (b) Compute Cov(Y(s), Y()) (c) Argue that {Y(t), t 2 0] is also Brownian motion...
In the following questions, let Bt denote a Brownian motion with
B0 = 0.
Consider a discrete time risk model U-2 + Σ Y" where {Yi) are iid random variables with N(2,1) distribution. Derive the lower bound for the survival probability, P(Un 〉 0, for all n 0, 1, 2, ). Justify your arguments.
Consider a discrete time risk model U-2 + Σ Y" where {Yi) are iid random variables with N(2,1) distribution. Derive the lower bound for the survival...
In the following questions, let Bt denote a Brownian motion with
B0 = 0.
Consider a discrete time risk model U-2 + Σ Y" where {Yi) are iid random variables with N(2,1) distribution. Derive the lower bound for the survival probability, P(Un 〉 0, for all n 0, 1, 2, ). Justify your arguments.
Consider a discrete time risk model U-2 + Σ Y" where {Yi) are iid random variables with N(2,1) distribution. Derive the lower bound for the survival...
Let X-(Xt ,0 < t < 1} be an arithmetic Brownian motion starting from 0 with drift parameter μ-0.2 and variance parameter ơ2-0.125. 1. Calculate the probability that X2 is between 0.1 and 0.5 2. Given that X 0.6, find the probability that X2 is between 0.1 and 0.5 3. Given that Xi- 0.2, find the covariance between X2 and X3
PROBLEM 2. [5 points] Let X(t) be a Brownian motion. Assume that the stock price follows the stochastic differential equation dS ơSdX+1Sdt. what stochastic differential equation does the stochastic process (a) Y 25, (b) Y = S (c) Y-es, (d) YeT-/S follow? In each cases express the coefficients of dX and dt in terms of Y rather than S. Use Ito's lemma
PROBLEM 2. [5 points] Let X(t) be a Brownian motion. Assume that the stock price follows the stochastic...
In the following questions, let Bt denote a Brownian motion with
B0 = 0.
Let Xn, n-0, 1, 2, denote a biased random walk given by X0 0 and Xn+1 = Xn+Yn+1, where {Yn} are i.i.d. random variables with N(-1,1) distribution. Show that MnX +2nXn +n(n - 1) is a martingale.
Let Xn, n-0, 1, 2, denote a biased random walk given by X0 0 and Xn+1 = Xn+Yn+1, where {Yn} are i.i.d. random variables with N(-1,1) distribution. Show that...
2. Solving the generalized geometric Brownian motion equation. Let S(t) be a positive stochastic process that satisfies the generalized geometric Brownian motion SDE dS(t) = u(t)S(t) dt + o(t)S(t) dW(t), where u(t) and o(t) are processes adapted to the filtration Ft, t > 0. (a) Use Itô’s lemma to compute d log S(t). Simplify so that you have a formula for d log S(t) that does not involve S(t). (b) Integrate the formula you obtained in (a), and then exponentiate...