Question

4. Hull and White [89] propose the two-factor model $$ \begin{aligned} & d U(t)=-\lambda_1 U(t) d t+\sigma_1 d \widetilde{B}_2(t), \\ & d R(t)=\left[\theta(t)+U(t)-\lambda_2 R(t)\right] d t+\sigma_2 d \widetilde{B}_1(t), \end{aligned} $$ where $\lambda_1, \lambda_2, \sigma_1$, and $\sigma_2$ are positive constants, $\theta(t)$ is a nonrandom function, and $\widetilde{B}_1(t)$ and $\widetilde{B}_2(t)$ are correlated Brownian motions with $d \widetilde{B}_1(t) d \widetilde{B}_2(t)=$ $\rho d t$ for some $\rho \in(-1,1)$. In this exercise, we discuss how to reduce this to the two-factor Vasicek model of Subsection 10.2.1, except that, instead of (10.2.6), the interest rate is given by $(10.7 .7)$, in which $\delta_0(t)$ is a nonrandom function of time. 10.7 Exercises 455 (i) Define $$ \begin{gathered} X(t)=\left[\begin{array}{l} U(t) \\ R(t) \end{array}\right], \quad K=\left[\begin{array}{cc} \lambda_1 & 0 \\ -1 & \lambda_2 \end{array}\right], \quad \Sigma=\left[\begin{array}{cc} \sigma_1 & 0 \\ 0 & \sigma_2 \end{array}\right] \\ \Theta(t)=\left[\begin{array}{c} 0 \\ \theta(t) \end{array}\right], \quad \widetilde{B}(t)=\left[\begin{array}{c} \widetilde{B}_1(t) \\ \widetilde{B}_2(t) \end{array}\right], \end{gathered} $$ so that $(10.7 .10)$ and $(10.7 .11)$ can be written in vector notation as $$ d X(t)=\Theta(t) d t-K X(t) d t+\Sigma d \widetilde{B}(t) . $$ Now set $$ \widehat{X}(t)=X(t)-e^{-K t} \int_0^t e^{K u} \theta(u) d u . $$ Show that $$ d \widehat{X}(t)=-K \widehat{X}(t) d t+\Sigma d \widetilde{B}(t) $$ (ii) With $$ C=\left[\begin{array}{cc} \frac{1}{\sigma_1} & 0 \\ -\frac{\rho}{\sigma_1 \sqrt{1-\rho^2}} & \frac{1}{\sigma_2 \sqrt{1-\rho^2}} \end{array}\right], $$ define $Y(t)=C \widehat{X}(t), \widetilde{W}(t)=C \Sigma \widetilde{B}(t)$. Show that the components of $\widetilde{W}_1(t)$ and $\widetilde{W}_2(t)$ are independent Brownian motions and $$ d Y(t)=-\Lambda Y(t)+d \widetilde{W}(t), $$ where $$ \Lambda=C K C^{-1}=\left[\begin{array}{cc} \lambda_1 & 0 \\ \frac{\rho \sigma_2\left(\lambda_2-\lambda_1\right)-\sigma_1}{\sigma_2 \sqrt{1-\rho^2}} & \lambda_2 . \end{array}\right] . $$ Equation (10.7.14) is the vector form of the canonical two-factor Vasicek equations (10.2.4) and (10.2.5). (iii) Obtain a formula for $R(t)$ of the form (10.7.7). What are $\delta_0(t), \delta_1$, and $\delta_2$ ?

    4. Hull and White [89] propose the two-factor model
$$
\begin{aligned}
& d U(t)=-\lambda_1 U(t) d t+\sigma_1 d \widetilde{B}_2(t), \\
& d R(t)=\left[\theta(t)+U(t)-\lambda_2 R(t)\right] d t+\sigma_2 d \widetilde{B}_1(t),
\end{aligned}
$$
where $\lambda_1, \lambda_2, \sigma_1$, and $\sigma_2$ are positive constants, $\theta(t)$ is a nonrandom function, and $\widetilde{B}_1(t)$ and $\widetilde{B}_2(t)$ are correlated Brownian motions with $d \widetilde{B}_1(t) d \widetilde{B}_2(t)=$ $\rho d t$ for some $\rho \in(-1,1)$. In this exercise, we discuss how to reduce this to the two-factor Vasicek model of Subsection 10.2.1, except that, instead of (10.2.6), the interest rate is given by $(10.7 .7)$, in which $\delta_0(t)$ is a nonrandom function of time.
10.7 Exercises
455
(i) Define
$$
\begin{gathered}
X(t)=\left[\begin{array}{l}
U(t) \\
R(t)
\end{array}\right], \quad K=\left[\begin{array}{cc}
\lambda_1 & 0 \\
-1 & \lambda_2
\end{array}\right], \quad \Sigma=\left[\begin{array}{cc}
\sigma_1 & 0 \\
0 & \sigma_2
\end{array}\right] \\
\Theta(t)=\left[\begin{array}{c}
0 \\
\theta(t)
\end{array}\right], \quad \widetilde{B}(t)=\left[\begin{array}{c}
\widetilde{B}_1(t) \\
\widetilde{B}_2(t)
\end{array}\right],
\end{gathered}
$$
so that $(10.7 .10)$ and $(10.7 .11)$ can be written in vector notation as
$$
d X(t)=\Theta(t) d t-K X(t) d t+\Sigma d \widetilde{B}(t) .
$$

Now set
$$
\widehat{X}(t)=X(t)-e^{-K t} \int_0^t e^{K u} \theta(u) d u .
$$

Show that
$$
d \widehat{X}(t)=-K \widehat{X}(t) d t+\Sigma d \widetilde{B}(t)
$$
(ii) With
$$
C=\left[\begin{array}{cc}
\frac{1}{\sigma_1} & 0 \\
-\frac{\rho}{\sigma_1 \sqrt{1-\rho^2}} & \frac{1}{\sigma_2 \sqrt{1-\rho^2}}
\end{array}\right],
$$
define $Y(t)=C \widehat{X}(t), \widetilde{W}(t)=C \Sigma \widetilde{B}(t)$. Show that the components of $\widetilde{W}_1(t)$ and $\widetilde{W}_2(t)$ are independent Brownian motions and
$$
d Y(t)=-\Lambda Y(t)+d \widetilde{W}(t),
$$
where
$$
\Lambda=C K C^{-1}=\left[\begin{array}{cc}
\lambda_1 & 0 \\
\frac{\rho \sigma_2\left(\lambda_2-\lambda_1\right)-\sigma_1}{\sigma_2 \sqrt{1-\rho^2}} & \lambda_2 .
\end{array}\right] .
$$

Equation (10.7.14) is the vector form of the canonical two-factor Vasicek equations (10.2.4) and (10.2.5).
(iii) Obtain a formula for $R(t)$ of the form (10.7.7). What are $\delta_0(t), \delta_1$, and $\delta_2$ ?
Show more…
Stochastic Calculus for Finance II : Continuous-Time Models
Stochastic Calculus for Finance II : Continuous-Time Models
Steven E. Shreve 1st Edition
Chapter 10, Problem 4 ↓

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$$ The system dynamics can then be written compactly as: $$ dX(t) = \Theta(t) dt - K X(t) dt + \Sigma d\widetilde{B}(t). $$  Show more…

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4. Hull and White [89] propose the two-factor model $$ \begin{aligned} & d U(t)=-\lambda_1 U(t) d t+\sigma_1 d \widetilde{B}_2(t), \\ & d R(t)=\left[\theta(t)+U(t)-\lambda_2 R(t)\right] d t+\sigma_2 d \widetilde{B}_1(t), \end{aligned} $$ where $\lambda_1, \lambda_2, \sigma_1$, and $\sigma_2$ are positive constants, $\theta(t)$ is a nonrandom function, and $\widetilde{B}_1(t)$ and $\widetilde{B}_2(t)$ are correlated Brownian motions with $d \widetilde{B}_1(t) d \widetilde{B}_2(t)=$ $\rho d t$ for some $\rho \in(-1,1)$. In this exercise, we discuss how to reduce this to the two-factor Vasicek model of Subsection 10.2.1, except that, instead of (10.2.6), the interest rate is given by $(10.7 .7)$, in which $\delta_0(t)$ is a nonrandom function of time. 10.7 Exercises 455 (i) Define $$ \begin{gathered} X(t)=\left[\begin{array}{l} U(t) \\ R(t) \end{array}\right], \quad K=\left[\begin{array}{cc} \lambda_1 & 0 \\ -1 & \lambda_2 \end{array}\right], \quad \Sigma=\left[\begin{array}{cc} \sigma_1 & 0 \\ 0 & \sigma_2 \end{array}\right] \\ \Theta(t)=\left[\begin{array}{c} 0 \\ \theta(t) \end{array}\right], \quad \widetilde{B}(t)=\left[\begin{array}{c} \widetilde{B}_1(t) \\ \widetilde{B}_2(t) \end{array}\right], \end{gathered} $$ so that $(10.7 .10)$ and $(10.7 .11)$ can be written in vector notation as $$ d X(t)=\Theta(t) d t-K X(t) d t+\Sigma d \widetilde{B}(t) . $$ Now set $$ \widehat{X}(t)=X(t)-e^{-K t} \int_0^t e^{K u} \theta(u) d u . $$ Show that $$ d \widehat{X}(t)=-K \widehat{X}(t) d t+\Sigma d \widetilde{B}(t) $$ (ii) With $$ C=\left[\begin{array}{cc} \frac{1}{\sigma_1} & 0 \\ -\frac{\rho}{\sigma_1 \sqrt{1-\rho^2}} & \frac{1}{\sigma_2 \sqrt{1-\rho^2}} \end{array}\right], $$ define $Y(t)=C \widehat{X}(t), \widetilde{W}(t)=C \Sigma \widetilde{B}(t)$. Show that the components of $\widetilde{W}_1(t)$ and $\widetilde{W}_2(t)$ are independent Brownian motions and $$ d Y(t)=-\Lambda Y(t)+d \widetilde{W}(t), $$ where $$ \Lambda=C K C^{-1}=\left[\begin{array}{cc} \lambda_1 & 0 \\ \frac{\rho \sigma_2\left(\lambda_2-\lambda_1\right)-\sigma_1}{\sigma_2 \sqrt{1-\rho^2}} & \lambda_2 . \end{array}\right] . $$ Equation (10.7.14) is the vector form of the canonical two-factor Vasicek equations (10.2.4) and (10.2.5). (iii) Obtain a formula for $R(t)$ of the form (10.7.7). What are $\delta_0(t), \delta_1$, and $\delta_2$ ?
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