HJMisspecificationDistance {intrinsicFRP}

Compute the HJ asset pricing model misspecification distance.

Description

Computes the Kan-Robotti (2008) <10.1016/j.jempfin.2008.03.003> squared model misspecification distance: ⁠square_distance = min_{d} (E[R] - Cov[R,F] * d)' * V[R]^{-1} * (E[R] - Cov[R,F] * d)⁠, where R denotes test asset excess returns and F risk factors, and computes the associated confidence interval. This model misspecification distance is a modification of the prominent Hansen-Jagannathan (1997) doi:10.1111/j.1540-6261.1997.tb04813.x distance, adapted to the use of excess returns for the test asset, and a SDF that is a linear function of demeaned factors. Clearly, computation of the confidence interval is obtained by means of an asymptotic analysis under potentially misspecified models, i.e., without assuming correct model specification. Details can be found in Kan-Robotti (2008) <10.1016/j.jempfin.2008.03.003>.

Usage

HJMisspecificationDistance(
  returns,
  factors,
  ci_coverage = 0.95,
  hac_prewhite = FALSE,
  check_arguments = TRUE
)

Arguments

returns

A ⁠n_observations x n_returns⁠ matrix of test asset excess returns.

factors

A ⁠n_observations x n_factors⁠ matrix of risk factors.

ci_coverage

A number indicating the confidence interval coverage probability. Default is 0.95.

hac_prewhite

A boolean indicating if the series needs pre-whitening 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

@return A list containing the squared misspecification-robust HJ distance in squared_distance, and the lower and upper confidence bounds in lower_bound and upper_bound, respectively.

Examples

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

# Compute the HJ model misspecification distance
hj_test = HJMisspecificationDistance(returns, factors)