Package: SVDNF 0.1.9

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.

Authors:Louis Arsenault-Mahjoubi [aut, cre], Jean-François Bégin [aut], Mathieu Boudreault [aut]

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SVDNF.pdf |SVDNF.html✨
SVDNF/json (API)

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6 exports 0.09 score 4 dependencies 1 scripts 259 downloads

Last updated 13 days agofrom:01187a88bc. Checks:OK: 9. Indexed: yes.

Exports:DNFDNFOptimdynamicsSVMextractVolPercmodelSimpars

Dependencies:latticeRcppxtszoo