% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/svp_test.R
\name{lmc_lev}
\alias{lmc_lev}
\title{LMC Test for Leverage in SV(p) Models}
\usage{
lmc_lev(
  y,
  p = 1,
  J = 10,
  N = 99,
  rho_null = 0,
  burnin = 500,
  rho_type = "pearson",
  del = 1e-10,
  trunc_lev = TRUE,
  wDecay = FALSE,
  Bartlett = FALSE,
  Amat = NULL,
  errorType = "Gaussian",
  logNu = FALSE,
  sigvMethod = "factored",
  winsorize_eps = 0
)
}
\arguments{
\item{y}{Numeric vector. Observed returns.}

\item{p}{Integer. Order of the volatility process. Default 1.}

\item{J}{Integer. Winsorizing parameter. Default 10.}

\item{N}{Integer. Number of Monte Carlo replications. Default 99.}

\item{rho_null}{Numeric. Value of \eqn{\rho} under the null. Default 0.}

\item{burnin}{Integer. Burn-in for simulation. Default 500.}

\item{rho_type}{Character. Correlation type. Default \code{"pearson"}.}

\item{del}{Numeric. Small constant for log transformation. Default \code{1e-10}.}

\item{trunc_lev}{Logical. Truncate leverage correlation estimate to
\code{[-0.999, 0.999]}. Default \code{TRUE}.}

\item{wDecay}{Logical. Use decaying weights. Default \code{FALSE}.}

\item{Bartlett}{Logical. If \code{TRUE}, use Bartlett kernel HAC weighting
matrix. If \code{FALSE}, use identity matrix. Default \code{FALSE}.}

\item{Amat}{Weighting matrix specification. \code{NULL} (default) for identity
weighting, \code{"Weighted"} for data-driven HAC, or a numeric matrix of
dimension \code{(p+3)x(p+3)} (Gaussian) or \code{(p+4)x(p+4)} (heavy-tail).
Takes precedence over \code{Bartlett}.}

\item{errorType}{Character. Error distribution: \code{"Gaussian"} (default),
\code{"Student-t"}, or \code{"GED"}.}

\item{logNu}{Logical. Use log-space for nu estimation (Student-t only).
Default \code{FALSE}.}

\item{sigvMethod}{Method for sigma_v estimation: \code{"factored"} (default),
\code{"direct"}, or \code{"hybrid"}.}

\item{winsorize_eps}{Number of extreme autocovariance lags to winsorize
(0 = none). Default 0.}
}
\value{
An object of class \code{"svp_test"}, a list containing:
\describe{
  \item{s0}{Test statistic from observed data.}
  \item{sN}{Simulated null distribution (vector of length N).}
  \item{pval}{Monte Carlo p-value.}
  \item{test_type}{Character string identifying the test.}
  \item{null_param}{Name of the parameter tested.}
  \item{null_value}{Value under the null hypothesis.}
  \item{call}{The matched call.}
}
}
\description{
Performs a Local Monte Carlo (LMC) test of the null hypothesis
\eqn{H_0: \rho = \rho_0} (typically \eqn{\rho_0 = 0}, i.e., no leverage)
using a GMM likelihood-ratio type statistic.
}
\examples{
\donttest{
y <- sim_svp(1000, phi = 0.95, sigy = 1, sigv = 0.2, leverage = TRUE, rho = -0.3)$y
test <- lmc_lev(y, p = 1, J = 10, N = 99)
print(test)
}

}
