rMinRQuar.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/realizedMeasures.R
\name{rMinRQuar}
\alias{rMinRQuar}
\title{An estimator of integrated quarticity from applying the minimum operator on blocks of two returns}
\usage{
rMinRQuar(rData, alignBy = NULL, alignPeriod = NULL, makeReturns = FALSE)
}
\arguments{
\item{rData}{an \code{xts} or \code{data.table} object containing returns or prices, possibly for multiple assets over multiple days}

\item{alignBy}{character, indicating the time scale in which \code{alignPeriod} is expressed. 
Possible values are: \code{"ticks"}, \code{"secs"}, \code{"seconds"}, \code{"mins"}, \code{"minutes"}, \code{"hours"}}

\item{alignPeriod}{positive numeric, indicating the number of periods to aggregate over. For example, to aggregate
based on a 5-minute frequency, set \code{alignPeriod = 5} and \code{alignBy = "minutes"}.}

\item{makeReturns}{boolean, should be \code{TRUE} when \code{rData} contains prices instead of returns. \code{FALSE} by default.}
}
\value{
\itemize{
\item In case the input is an \code{xts} object with data from one day, a numeric of the same length as the number of assets.
\item If the input data spans multiple days and is in \code{xts} format, an \code{xts} will be returned.
\item If the input data is a \code{data.table} object, the function returns a \code{data.table} with the same column names as the input data, containing the date and the realized measures.
}
}
\description{
Calculate the rMinRQuar, defined in Andersen et al. (2012).
Assume there are \eqn{N} equispaced returns \eqn{r_{t,i}} in period \eqn{t}, \eqn{i=1, \ldots,N}.
Then, the rMinRQuar is given by
\deqn{
  \mbox{rMinRQuar}_{t}=\frac{\pi N}{3 \pi - 8} \left(\frac{N}{N-1}\right) \sum_{i=1}^{N-1} \mbox{min}(|r_{t,i}| ,|r_{t,i+1}|)^4
}
}
\examples{
rq <- rMinRQuar(rData = sampleTData[, list(DT, PRICE)], alignBy = "minutes",
            alignPeriod = 5, makeReturns = TRUE)
rq
}
\references{
Andersen, T. G., Dobrev, D., and Schaumburg, E. (2012). Jump-robust volatility estimation using nearest neighbor truncation. \emph{Journal of Econometrics}, 169, 75-93.
}
\author{
Giang Nguyen, Jonathan Cornelissen, Kris Boudt, and Emil Sjoerup
}