Package: intrinsicFRP 2.1.0

intrinsicFRP: An R Package for Factor Model Asset Pricing

Functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>, which is a modification of the prominent Hansen-Jagannathan (1997) <doi:10.1111/j.1540-6261.1997.tb04813.x> distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912> covariance estimator.

Authors:Alberto Quaini [aut, cre, cph]

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intrinsicFRP.pdf |intrinsicFRP.html
intrinsicFRP/json (API)
NEWS

# Install 'intrinsicFRP' in R:
install.packages('intrinsicFRP', repos = c('https://a91quaini.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/a91quaini/intrinsicfrp/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • factors - Factors - monthly observations from '07/1963' to '02/2024'
  • returns - Test Asset Excess Returns - monthly observations from '07/1963' to '02/2024'
  • risk_free - Risk free - monthly observations from '07/1963' to '02/2024'

On CRAN:

factor-modelsfactor-selectionfinanceidentification-testsmisspecificationrcpparmadillorisk-premium

11 exports 7 stars 2.23 score 11 dependencies 1 scripts 174 downloads

Last updated 2 months agofrom:1f2f3fc1b7. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-win-x86_64OKAug 28 2024
R-4.5-linux-x86_64OKAug 28 2024
R-4.4-win-x86_64OKAug 28 2024
R-4.4-mac-x86_64OKAug 28 2024
R-4.4-mac-aarch64OKAug 28 2024
R-4.3-win-x86_64OKAug 28 2024
R-4.3-mac-x86_64OKAug 28 2024
R-4.3-mac-aarch64OKAug 28 2024

Exports:ChenFang2019BetaRankTestFGXFactorsTestFRPGiglioXiu2021RiskPremiaGKRFactorScreeningHACcovarianceHJMisspecificationDistanceIterativeKleibergenPaap2006BetaRankTestOracleTFRPSDFCoefficientsTFRP

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppArmadilloRcppEigenshapesurvival