TFRP {intrinsicFRP}

Tradable factor risk premia.

Description

Computes tradable factor risk premia from data on factors F and test asset excess returns R: TFRP = Cov[F, R] * Var[R]^{-1} * E[R]; which are by construction the negative covariance of factors F with the SDF projection on asset returns, i.e., the minimum variance SDF. Optionally computes the corresponding heteroskedasticity and autocorrelation robust standard errors 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. All details are found in Quaini-Trojani-Yuan (2023) doi:10.2139/ssrn.4574683.

Usage

TFRP(
  returns,
  factors,
  include_standard_errors = FALSE,
  hac_prewhite = FALSE,
  check_arguments = TRUE
)

Arguments

returns

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

factors

A ⁠n_observations x n_factors⁠-dimensional matrix of factors.

include_standard_errors

A boolean: TRUE if you want to compute the tradable factor risk premia HAC standard errors; FALSE otherwise. Default is FALSE.

hac_prewhite

A boolean indicating if the series needs prewhitening by fitting an AR(1) in the internal heteroskedasticity and autocorrelation robust covariance (HAC) estimation. Default is false.

check_arguments

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

Value

A list containing n_factors-dimensional vector of tradable factor risk premia in "risk_premia"; if include_standard_errors=TRUE, then it further includes n_factors-dimensional vector of tradable factor risk premia standard errors in "standard_errors".

Examples

# import package data on 6 risk factors and 42 test asset excess returns
factors = intrinsicFRP::factors[,-1]
returns = intrinsicFRP::returns[,-1]

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