Let X(t) = μt + σB(t) be a standard Brown motion
Show that:
Cov(X(s), X(t)) = σ2 min(s, t)
Let X(t) = μt + σB(t) be a standard Brown motion Show that: Cov(X(s), X(t)) =...
Let be a standard Brown motion. a) Show that b) What is the correlation of X(t) and X(s) ? We were unable to transcribe this imageCov(X(s), X(t)) σ2 min(s, t) Cov(X(s), X(t)) σ2 min(s, t)
3. Let W(t be standard Brownian motion and let to > 0. Consider the random variable Min(to) min{W(s) 0 s< to}. Compute the cumulative distribution function of Min(to) 3. Let W(t be standard Brownian motion and let to > 0. Consider the random variable Min(to) min{W(s) 0 s
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...
Let W - {Wi,0< t < ) represent a standard Brownian motion Show that the process Z(s)-(zt-W f.0 < t-1) is a standard Brownian motion, where s > 0 is fixed
3. Let U-Bt- tB be Brownian bridge on [0, 1], where {BiJosesi is a Brownian process (i) Show E(Ut0 (ii) Show Cov(U,, Ut) s(1- t) for 0 s ts1. (ii) Let Xg(t)B Find functions g and h such that X, has the same covariance as a Brownian bridge. 3. Let U-Bt- tB be Brownian bridge on [0, 1], where {BiJosesi is a Brownian process (i) Show E(Ut0 (ii) Show Cov(U,, Ut) s(1- t) for 0 s ts1. (ii) Let Xg(t)B...
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:
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
4. [20 points] Let {B(t):t0 be a standard Brownian motion. Define a stochastic process (X (t):t20) by the formulas X (t) = tB(1 + t-1)-tB(1), x(0) = 0, t > 0, You may take for granted the fact that imt-«HX(t) = 0, with probability 1 (b) Explain why [X():t20 is a standard Brownian motion 4. [20 points] Let {B(t):t0 be a standard Brownian motion. Define a stochastic process (X (t):t20) by the formulas X (t) = tB(1 + t-1)-tB(1), x(0)...
Let X(t), t ≥ 0 be a Brownian motion process with drift parameter µ = 3 and variance parameter σ2 = 9. If X(0) = 10, find P(X(2) > 20).
2. Let Xand Y be random variables with joint moment generating function M(s,t) 0.3+0.1es + 0.4e +0.2 es*t (a) What are E(X) and E(Y)? (b) Find Cov(X,Y) 2. Let Xand Y be random variables with joint moment generating function M(s,t) 0.3+0.1es + 0.4e +0.2 es*t (a) What are E(X) and E(Y)? (b) Find Cov(X,Y)