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SVDNF  

Discrete Nonlinear Filtering for Stochastic Volatility Models
View on CRAN: Click here


Download and install SVDNF package within the R console
Install from CRAN:
install.packages("SVDNF")

Install from Github:
library("remotes")
install_github("cran/SVDNF")

Install by package version:
library("remotes")
install_version("SVDNF", "0.1.8")



Attach the package and use:
library("SVDNF")
Maintained by
Louis Arsenault-Mahjoubi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-11
Latest Update: 2023-08-08
Description:
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>.
How to cite:
Louis Arsenault-Mahjoubi (2022). SVDNF: Discrete Nonlinear Filtering for Stochastic Volatility Models. R package version 0.1.8, https://cran.r-project.org/web/packages/SVDNF
Previous versions and publish date:
0.1.0 (2022-10-11 08:12), 0.1.1 (2022-11-07 17:30), 0.1.2 (2023-01-06 21:20), 0.1.3 (2023-01-14 23:20), 0.1.4 (2023-03-01 18:50), 0.1.5 (2023-05-31 16:40), 0.1.6 (2023-07-13 10:40), 0.1.7 (2023-07-21 05:30)
Other packages that cited SVDNF R package
View SVDNF citation profile
Other R packages that SVDNF depends, imports, suggests or enhances
Functions, R codes and Examples using the SVDNF R package
Some associated functions: DNF.dynamicsSVM . DNFOptim . dynamicsSVM . modelSim.dynamicsSVM . plot.SVDNF . 
Some associated R codes: DNF.R . DNFOptim.R . RcppExports.R . dynamicsSVM.R . gridMaker.R . modelSim.R . plot.SVDNF.R . probCalculator.R .  Full SVDNF package functions and examples
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