General Affine DiffusionsΒΆ
A jump-diffusion process is a Markov process solving the stochastic differential equationd
\[Y_{t}=\mu\left(Y_{t},\theta_{0}\right)dt
+\sigma\left(Y_{t},\theta_{0}\right)dW_{t}.\]
A discount-rate function \(R:D\to\mathbb{R}\) is an affine function of the state
\[R\left(Y\right)=\rho_{0}+\rho_{1}\cdot Y,\]
for \(\rho=\left(\rho_{0},\rho_{1}\right)\in\mathbb{R} \times\mathbb{R}^{N}\).
The affine dependence of the drift and diffusion coefficients of \(Y\) are determined by coefficients \(\left(K,H\right)\) defined by:
\[\mu\left(Y\right)=K_{0}+K_{1}Y,\]
for \(K=\left(K_{0},K_{1}\right)\in\mathbb{R}^{N}\times\mathbb{R}^{N\times N}\),
and
\[\left[\sigma\left(Y\right)\sigma\left(Y\right)^{\prime}\right]_{ij}
=\left[H_{0}\right]_{ij}+\left[H_{1}\right]_{ij}\cdot Y,\]
for \(H=\left(H_{0},H_{1}\right)\in\mathbb{R}^{N\times N} \times\mathbb{R}^{N\times N\times N}\).
Here
\[\left[H_{1}\right]_{ij}\cdot Y=\sum_{k=1}^{N}\left[H_{1}\right]_{ijk}Y_{k}.\]
A characteristic \(\chi=\left(K,H,\rho\right)\) captures both the distribution of \(Y\) as well as the effects of any discounting.