ajdmom.mdl_svcj.cond2_mom

Conditional Moments for the SVCJ model, given \(v_0\) and the realized jumps in the variance.

Functions

comb_poly(index)	multiply together those conditional normal distribution moments
m(l, par)	conditional moment in scalar
mnorm_cond(n)	conditional normal distribution moment \(J_i^s|J_i^v \sim \mathcal{N} (\mu_s+rhoJ J_i^v, \sigma_s^2)\)
moment_IZs(m, J)	moment \(\mathbb{E}[(IZ_t^{s})^{m}|z^v(u), 0\le u \le t]\)
moment_y(l, J)
moments_y_to(l, J)	conditional moments of \(y_t\) of orders \(0:l\)
poly2num(poly, par)	Decode poly back to scalar