FGXFactorsTest {intrinsicFRP}

Testing for the pricing contribution of new factors.

Description

Computes the three-pass procedure of Feng Giglio and Xiu (2020) doi:org/10.1111/jofi.12883, which evaluates the contribution to cross-sectional pricing of any new factors on top of a set of control factors. The third step is a OLS regression of average returns on the covariances between asset returns and the new factors, as well as the control factors selected in either one of the first two steps. The first stwo steps consists in (i) a Lasso regression of average returns on the ovariances between asset returns and all control factors and (ii) a Lasso regression of the covariances between asset returns and the new factors on the ovariances between asset returns and all control factors. The second selection aims at correcting for potential omitted variables in the first selection. Tuning of the penalty parameters in the Lasso regressions is performed via Cross Validation (CV). Standard errors are computed following Feng Giglio and Xiu (2020) using the Newey-West (1994) doi:10.2307/2297912 plug-in procedure to select the number of relevant lags, i.e., n_lags = 4 * (n_observations/100)^(2/9). For the standard error computations, the function allows to internally pre-whiten the series by fitting a VAR(1), i.e., a vector autoregressive model of order 1.

Usage

FGXFactorsTest(
  gross_returns,
  control_factors,
  new_factors,
  n_folds = 5,
  check_arguments = TRUE
)

Arguments

gross_returns

A ⁠n_observations x n_returns⁠-dimensional matrix of test asset gross returns.

control_factors

A ⁠n_observations x n_control_factors⁠-dimensional matrix of control or benchmark factors.

new_factors

A ⁠n_observations x n_new_factors⁠-dimensional matrix of new factors.

n_folds

An integer indicating the number of k-fold for cross validation. Default is 5.

check_arguments

A boolean TRUE for internal check of all function arguments; FALSE otherwise. Default is TRUE.

Value

A list containing the n_new_factors-dimensional vector of SDF coefficients in "sdf_coefficients" and corresponding standard errors in "standard_errors"; it also returns the index of control factors that are selected by the two-step selection procedure.

Examples

# import package data on 6 risk factors and 42 test asset excess returns
control_factors = intrinsicFRP::factors[,2:4]
new_factors = intrinsicFRP::factors[,5:7]
returns = intrinsicFRP::returns[,-1]
RF = intrinsicFRP::risk_free[,-1]

gross_returns = returns + 1 + RF

output = FGXFactorsTest(
  gross_returns,
  control_factors,
  new_factors
)