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.
| Version: |
0.1.9 |
| Imports: |
Rcpp (≥ 1.0.9), methods, zoo, xts |
| LinkingTo: |
Rcpp |
| Published: |
2024-09-04 |
| DOI: |
10.32614/CRAN.package.SVDNF |
| Author: |
Louis Arsenault-Mahjoubi [aut, cre],
Jean-François Bégin [aut],
Mathieu Boudreault [aut] |
| Maintainer: |
Louis Arsenault-Mahjoubi <larsenau at sfu.ca> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| In views: |
Finance |
| CRAN checks: |
SVDNF results |
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