% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/svp_test.R
\name{mmc_t}
\alias{mmc_t}
\title{MMC Test for Student-t Tail Parameter}
\usage{
mmc_t(
  y,
  p = 1,
  J = 10,
  N = 99,
  nu_null,
  burnin = 500,
  eps = NULL,
  threshold = 1,
  method = "pso",
  maxit = NULL,
  del = 1e-10,
  wDecay = FALSE,
  Bartlett = FALSE,
  Amat = NULL,
  logNu = TRUE,
  direction = c("two-sided", "less", "greater"),
  sigvMethod = "factored",
  winsorize_eps = 0
)
}
\arguments{
\item{y}{Numeric vector. Observed returns.}

\item{p}{Integer. AR 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{nu_null}{Numeric. Value of \eqn{\nu} under the null hypothesis.}

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

\item{eps}{Numeric vector. Half-width of search region around estimated nuisance
parameters. Must have length \code{p+2} (one entry per nuisance parameter:
\eqn{\phi_1,\ldots,\phi_p, \sigma_y, \sigma_v}). Default \code{rep(0.3, p+2)}.}

\item{threshold}{Numeric. Target p-value. Default 1.}

\item{method}{Character. Optimization method: \code{"pso"} or \code{"GenSA"}.
Default \code{"pso"}.}

\item{maxit}{Integer. Maximum iterations/evaluations. Default depends on method.}

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

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

\item{Bartlett}{Logical. Use Bartlett kernel HAC for weighting matrix.
Default \code{FALSE}.}

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

\item{logNu}{Logical. Use log-space for nu estimation. Default \code{TRUE}.}

\item{direction}{Character. Test direction: \code{"two-sided"} (default),
\code{"less"} (H1: nu < nu_null), or \code{"greater"} (H1: nu > nu_null).
Uses signed root of the LR statistic for one-sided tests.}

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

\item{winsorize_eps}{Numeric. Winsorization threshold for moment conditions.
Default 0 (no winsorization).}
}
\value{
A list with optimization output including \code{value} (maximized p-value)
  and \code{par} (nuisance parameters at the maximum).
}
\description{
Performs a Maximized Monte Carlo (MMC) test of \eqn{H_0: \nu = \nu_0}
by maximizing the MC p-value over nuisance parameters (phi, sigma_y, sigma_v).
}
\examples{
\donttest{
y <- sim_svp(1000, phi = 0.95, sigy = 1, sigv = 0.2, errorType = "Student-t", nu = 5)$y
mmc <- mmc_t(y, p = 1, J = 10, N = 19, nu_null = 5, method = "pso", maxit = 10)
mmc$value
}

}
