% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{shanghai_stock.its}
\alias{shanghai_stock.its}
\title{Shanghai Stock Exchange Composite Index Daily High/Low Interval Time Series}
\format{
A data frame with 970 observations and 3 variables:
\itemize{
    \item \code{date}: Trading date (Date class).
    \item \code{low}: Daily low value of the SSE Composite Index.
    \item \code{high}: Daily high value of the SSE Composite Index.
}
}
\source{
Yahoo Finance, ticker \code{000001.SS}. Downloaded via the
\pkg{quantmod} package.
}
\usage{
data(shanghai_stock.its)
}
\description{
Daily high and low values of the Shanghai Stock Exchange Composite Index
(SSE Composite) from January 2, 2019 to December 30, 2022 (970 trading
days). This dataset matches the period used by Yang, Zhang and Wang
(2025) for interval time series forecasting.
}
\details{
The SSE Composite Index is the most commonly used indicator to reflect
the performance of the Shanghai Stock Exchange. It tracks all stocks
(A-shares and B-shares) listed on the exchange. This dataset covers a
period that includes the COVID-19 pandemic and its market impacts,
providing a rich testbed for evaluating interval forecasting models
under extreme volatility.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 970 \cr
  \strong{Variables (p)} \tab 3 (date, low, high) \cr
  \strong{Subject area} \tab Finance \cr
  \strong{Symbolic format} \tab Interval time series \cr
  \strong{Analytical tasks} \tab Forecasting, Time series analysis
}
}

\examples{
data(shanghai_stock.its)
head(shanghai_stock.its)
plot(shanghai_stock.its$date, shanghai_stock.its$high, type = "l",
     col = "red", ylab = "Index Value", xlab = "Date",
     main = "Shanghai Composite Daily High/Low (2019-2022)")
lines(shanghai_stock.its$date, shanghai_stock.its$low, col = "blue")
legend("topleft", c("High", "Low"), col = c("red", "blue"), lty = 1)
}
\references{
Yang, W., Zhang, S. and Wang, S. (2025). On smooth transition interval
autoregressive models. \emph{Journal of Forecasting}, \strong{44}(2),
310--332.
}
\keyword{datasets}
\keyword{finance}
\keyword{interval}
\keyword{timeseries}
