Estimation of affine asset pricing models using the empirical characteristic function

The known functional form of the conditional characteristic function (CCF) of discretely sampled observations from an affine diffusion is used to develop computationally tractable and asymptotically efficient estimators of the parameters of affine diffusions, and of asset pricing models in which the state vectors follow affine diffusions. Both ‘time-domain’ estimators, based on Fourier inversion of the CCF, and ‘frequency-domain’ estimators, based directly on the CCF, are constructed. A method-of-moments estimator based on the CCF is shown to approximate the efficiency of maximum likelihood for affine diffusion and asset pricing models.