SVDNF - Discrete Nonlinear Filtering for Stochastic Volatility Models
Implements the discrete nonlinear filter (DNF) of Kitagawa
(1987) <doi:10.1080/01621459.1987.10478534> to a wide class of
stochastic volatility (SV) models with return and volatility
jumps following the work of Bégin and Boudreault (2021)
<doi:10.1080/10618600.2020.1840995>. Offers several built-in SV
models and a flexible framework for users to create customized
models by specifying drift and diffusion functions along with a
jump arrival distribution for the return and volatility
dynamics. Allows for the estimation of factor models with
stochastic volatility (e.g., heteroskedastic volatility CAPM)
by incorporating expected return predictors. `Includes
functions to compute likelihood evaluations, filtering and
prediction distribution estimates, maximum likelihood parameter
estimates, to simulate data from built-in and custom SV models
with jumps, and to forecast future returns and volatility
values using Monte Carlo simulation from a given SV model.