Affine process ($\log {\mathbb{E}}_x{e}^{\theta X_1}=A(\theta )x+B(\theta )$)

An affine process is a time-homogeneous Markov process whose conditional moment-generating function is exponential-affine in its starting state. In discrete time one writes $\log {\mathbb{E}}_x{e}^{\theta X_1}=A(\theta )x+B(\theta )$ on the finite exponential moment domain. Continuous-time definitions allow the functions to depend also on elapsed time.

An autoregressive process of order one with independent and identically distributed random variables as innovations is an affine process when the innovation exponential moments are finite. Its one-step coefficients are $A(\theta )=a\theta$ and $B(\theta )=b\theta +\psi (\theta )$, where $\psi$ is the innovation cumulant-generating function.

For a scalar discrete-time affine process, the conditional cumulant-generating function of ${\sum }_{j=1}^t{\theta }_j{X}_j$ is affine in $X_0$. Backward effective parameters $q_t={\theta }_t$, $q_j={\theta }_j+A(q_{j+1})$ give coefficients $A(q_1)$ and ${\sum }_{j}B(q_j)$.