README

Installation

Building from CRAN

Package intrinsicFRP is on CRAN (The Comprehensive R Archive Network), hence the latest release can be easily installed from the R command line via

install.packages("intrinsicFRP")

Building from source

To install the latest (possibly unstable) development version from GitHub, you can pull this repository and install it from the R command line via

# if you already have package `devtools` installed, you can skip the next line
install.packages("devtools")
devtools::install_github("a91quaini/intrinsicFRP")

Package intrinsicFRP contains C++ code that needs to be compiled, so you may need to download and install the necessary tools for MacOS or the necessary tools for Windows.

Main functions:

TFRP() [Prevously: IFRP]: Computes tradable factor risk premia from data on factors and test asset excess returns. Optionally computes the corresponding heteroskedasticity and autocorrelation robust standard errors using the Newey-West (1994) plug-in procedure to select the number of relevant lags, i.e., n_lags = 4 * (n_observations/100)^(2/9).

OracleTFRP() [Prevously: OptimalAdaptiveIFRP]: Computes optimal Oracle tradable factor risk premia for various penalty parameter values from data on factors and test asset excess returns. Tuning is performed via Generalized Cross Validation (GCV), Cross Validation (CV) or Rolling Validation (RV). Adaptive weights are based on the correlation between factors and returns, on the regression coefficients of returns on factors or on the first-step intrinsic risk premia estimator. Optionally computes the corresponding heteroskedasticity and autocorrelation robust standard errors using the Newey-West (1994) plug-in procedure to select the number of relevant lags, i.e., n_lags = 4 * (n_observations/100)^(2/9).

FRP(): Computes Fama MachBeth (1973) or misspecification-robust factor risk premia of Kan Robotti Shanken (2013) from data on factors and test asset excess returns. Optionally computes the corresponding heteroskedasticity and autocorrelation robust standard errors using the Newey-West (1994) plug-in procedure to select the number of relevant lags, i.e., n_lags = 4 * (n_observations/100)^(2/9).

ChenFang2019BetaRankTest(): Computes the Chen fang 2019 rank statistic and p-value of the null that the matrix of regression loadings of test asset excess returns on risk factors has reduced rank.

HJMisspecificationTest(): Computes the Hansen-Jagannatan misspecification statistic and p-value of an asset pricing model from test asset excess returns and risk factors.

For usage details, type ?FunctionName in the R console, e.g.:

?TFRP

Example

# import package data on 6 risk factors and 42 test asset excess returns
factors = intrinsicFRP::factors[,c(2,3,4)]
returns = intrinsicFRP::returns[,-1]

# simulate a useless factor and add it to the matrix of factors
set.seed(23)
factors = cbind(factors, stats::rnorm(nrow(factors), sd = stats::sd(factors[,1])))
colnames(factors) = c(colnames(intrinsicFRP::factors[,c(2,3,4)]), "Usless")

# compute tradable factor risk premia and their standard errors
tfrp = TFRP(returns, factors, include_standard_errors = TRUE)

# compute the GLS factor risk premia of Kan Robotti and Shanken (2013) and their
# standard errors
krs_frp = FRP(returns, factors, include_standard_errors = TRUE)

# set penalty parameters
penalty_parameters = seq(1e-4, 1e-2, length.out = 1000)

# compute Oracle tradable factor risk premia and their standard errors
oracle_tfrp = OracleTFRP(
  returns,
  factors,
  penalty_parameters,
  include_standard_errors = TRUE,
)

Compare risk premia estimates of misspecification-robust factor risk premia (KRS), tradable factor risk premia (TFRP) and Oracle TFRP (O-TFRP):

References

Kan, R., Robotti, C. and Shanken, J., 2013. Pricing model performance and the two‐pass cross‐sectional regression methodology. The Journal of Finance, 68(6), pp.2617-2649.