SVDNF: Discrete Nonlinear Filtering for Stochastic Volatility Models

Generates simulated paths from various financial stochastic volatility models with jumps and applies the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to compute likelihood evaluations, filtering distribution estimates, and maximum likelihood parameter estimates. The algorithm is implemented following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995>.

Version: 0.1.1
Imports: Rcpp (≥ 1.0.9), methods
LinkingTo: Rcpp
Published: 2022-11-07
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

Documentation:

Reference manual: SVDNF.pdf

Downloads:

Package source: SVDNF_0.1.1.tar.gz
Windows binaries: r-devel: SVDNF_0.1.1.zip, r-release: SVDNF_0.1.1.zip, r-oldrel: SVDNF_0.1.1.zip
macOS binaries: r-release (arm64): SVDNF_0.1.1.tgz, r-oldrel (arm64): SVDNF_0.1.1.tgz, r-release (x86_64): SVDNF_0.1.1.tgz, r-oldrel (x86_64): SVDNF_0.1.1.tgz
Old sources: SVDNF archive

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