LFM (version 0.3.3)

factor.tests: 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)

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

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%.

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")