Discrete Nonlinear Filtering for Stochastic Volatility Models
Discrete Nonlinear Filtering Algorithm for Stochastic Volatility Model...
Discrete Nonlinear Filter Maximum Likelihood Estimation Function
Stochastic Volatility Models Dynamics
Extract Filtering and Prediction Distribution Percentiles
Extract Log-Likelihood for SVDNF and DNFOptim Objects
Simulation from Stochastic Volatility Models with Jumps
Parameters Names and Order for Stochastic Volatility Models with Jumps
Plot Predictions from DNFOptim or SVDNF Objects
DNF Filtering Distribution Plot Function
Predict Method for DNFOptim and SVDNF Objects
Summarizing Stochastic Volatility Model Fits from the Discrete Nonline...
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.