Question

(Moment-generating function for Cox-Ingersoll-Ross process). (i) Let $W_1, \ldots, W_d$ be independent Brownian motions and let $a$ and $\sigma$ be positive constants. For $j=1, \ldots, d$, let $X_j(t)$ be the solution of the OrnsteinUhlenbeck stochastic differential equation 6.9 Exercises 287 $$ d X_j(t)=-\frac{b}{2} X_j(t) d t+\frac{1}{2} \sigma d W_j(t) . $$ Show that $$ X_j(t)=e^{-\frac{1}{2} b t}\left[X_j(0)+\frac{\sigma}{2} \int_0^t e^{\frac{1}{2} b u} d W_j(u)\right] . $$ Show further that for fixed $t$, the random variable $X_j(t)$ is normal with $$ \mathbf{E} X_j(t)=e^{-\frac{1}{2} b t} X_j(0), \quad \operatorname{Var}\left(X_j(t)\right)=\frac{\sigma^2}{4 b}\left[1-e^{-b t}\right] $$

     (Moment-generating function for Cox-Ingersoll-Ross process).
(i) Let $W_1, \ldots, W_d$ be independent Brownian motions and let $a$ and $\sigma$ be positive constants. For $j=1, \ldots, d$, let $X_j(t)$ be the solution of the OrnsteinUhlenbeck stochastic differential equation
6.9 Exercises
287
$$
d X_j(t)=-\frac{b}{2} X_j(t) d t+\frac{1}{2} \sigma d W_j(t) .
$$

Show that
$$
X_j(t)=e^{-\frac{1}{2} b t}\left[X_j(0)+\frac{\sigma}{2} \int_0^t e^{\frac{1}{2} b u} d W_j(u)\right] .
$$

Show further that for fixed $t$, the random variable $X_j(t)$ is normal with
$$
\mathbf{E} X_j(t)=e^{-\frac{1}{2} b t} X_j(0), \quad \operatorname{Var}\left(X_j(t)\right)=\frac{\sigma^2}{4 b}\left[1-e^{-b t}\right]
$$
Show more…
Stochastic Calculus for Finance II : Continuous-Time Models
Stochastic Calculus for Finance II : Continuous-Time Models
Steven E. Shreve 1st Edition
Chapter 6, Problem 6 ↓

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Step 1

\] To solve this, we use the method of integrating factors. Define the integrating factor: \[ \mu(t) = e^{\int -\frac{b}{2} dt} = e^{-\frac{b}{2} t}. \] Multiply both sides of the SDE by $\mu(t)$: \[ \mu(t) dX_j(t) = \mu(t) \left(-\frac{b}{2} X_j(t) dt +  Show more…

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(Moment-generating function for Cox-Ingersoll-Ross process). (i) Let $W_1, \ldots, W_d$ be independent Brownian motions and let $a$ and $\sigma$ be positive constants. For $j=1, \ldots, d$, let $X_j(t)$ be the solution of the OrnsteinUhlenbeck stochastic differential equation 6.9 Exercises 287 $$ d X_j(t)=-\frac{b}{2} X_j(t) d t+\frac{1}{2} \sigma d W_j(t) . $$ Show that $$ X_j(t)=e^{-\frac{1}{2} b t}\left[X_j(0)+\frac{\sigma}{2} \int_0^t e^{\frac{1}{2} b u} d W_j(u)\right] . $$ Show further that for fixed $t$, the random variable $X_j(t)$ is normal with $$ \mathbf{E} X_j(t)=e^{-\frac{1}{2} b t} X_j(0), \quad \operatorname{Var}\left(X_j(t)\right)=\frac{\sigma^2}{4 b}\left[1-e^{-b t}\right] $$
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Key Concepts

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Stochastic Differential Equations
These are equations in which the evolution of a variable is influenced by both deterministic and random components. They are fundamental tools for modeling systems under the influence of random fluctuations or noise, and tools like ItƓ calculus are used to analyze and solve them.
Ornstein-Uhlenbeck Process
A classic example of a mean-reverting stochastic process, the Ornstein-Uhlenbeck process is described by a linear stochastic differential equation. It characterizes systems that tend to drift back to a long-term mean, and its solution often involves an exponential decay factor combined with integration with respect to a Brownian motion, demonstrating how uncertainty dissipates over time.
Brownian Motion
Brownian motion, or Wiener process, is a continuous-time stochastic process with independent, normally distributed increments. It is the fundamental building block for modeling randomness in continuous time and serves as the driving noise in many stochastic differential equations, including the Ornstein-Uhlenbeck process.
ItƓ Integral
The ItƓ integral is the stochastic counterpart of the conventional integral, defined for integrating with respect to Brownian motion or other martingales. It allows the transformation of stochastic differential equations into integral form, facilitating the computation of explicit solutions and the analysis of the distributional properties of the processes.

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