factor.tests: Factor Model Testing with Wald, GRS, PY tests and FDR control

In LFM: Laplace Factor Model Analysis and Evaluation

Factor Model Testing with Wald, GRS, PY tests and FDR control

Description

Performs comprehensive factor model testing including joint tests (Wald, GRS, PY), individual asset t-tests, and False Discovery Rate control.

Usage

factor.tests(ret, fac, q.fdr = 0.05)

Arguments

ret	A T × N matrix representing the excess returns of N assets at T time points.
fac	A T × K matrix representing the returns of K factors at T time points.
q.fdr	The significance level for FDR (False Discovery Rate) testing, defaulting to 5%.

Value

A list containing the following components:

alpha	N-vector of estimated alphas for each asset
tstat	N-vector of t-statistics for testing individual alphas
pval	N-vector of p-values for individual alpha tests
Wald	Wald test statistic for joint alpha significance
p_Wald	p-value for Wald test
GRS	GRS test statistic (finite-sample F-test)
p_GRS	p-value for GRS test
PY	Pesaran and Yamagata test statistic
p_PY	p-value for PY test
reject_fdr	Logical vector indicating which assets have significant alphas after FDR correction
fdr_p	Adjusted p-values using Benjamini-Hochberg procedure
power_proxy	Number of significant assets after FDR correction

Examples

set.seed(42)
T <- 120
N <- 25
K <- 3
fac <- matrix(rnorm(T * K), T, K)
beta <- matrix(rnorm(N * K), N, K)
alpha <- rep(0, N)
alpha[1:3] <- 0.4 / 100  # 3 non-zero alphas
eps <- matrix(rnorm(T * N, sd = 0.02), T, N)
ret <- alpha + fac %*% t(beta) + eps
results <- factor.tests(ret, fac, q.fdr = 0.05)

# View results
cat("Wald test p-value:", results$p_Wald, "\n")
cat("GRS test p-value:", results$p_GRS, "\n")
cat("PY test p-value:", results$p_PY, "\n")
cat("Significant assets after FDR:", results$power_proxy, "\n")

LFM documentation built on Dec. 6, 2025, 5:06 p.m.