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.
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
[aut, cre, cph]