R/llq2022-data.R

In forecastdom: (Un)Conditional Forecast Dominance

#' S&P 500 Realized-Variance Forecasts (Li, Liao & Quaedvlieg, 2022)
#'
#' Daily 5-minute realized variance of the S&P 500 with one-step-ahead
#' out-of-sample forecasts from six competing models, plus lagged VIX as
#' a conditioning variable. The forecasts and realized variances are
#' from the replication package of Li, Liao and Quaedvlieg (2022) on
#' Zenodo; the underlying RV series is from Bollerslev, Patton and
#' Quaedvlieg (2016).
#'
#' @format A data frame with 3,071 rows and 9 variables. Sample period
#' is 2001-05-11 to 2013-08-30 (daily, trading days).
#' \describe{
#'   \item{date}{Trading-day \code{Date}.}
#'   \item{rv}{Realized variance of the S&P 500 (5-minute returns), in
#'     percent-squared units.}
#'   \item{AR1}{AR(1) forecast of \code{rv}.}
#'   \item{AR22}{AR(22) forecast of \code{rv}.}
#'   \item{AR22_Lasso}{AR(22) forecast with adaptive lasso (Matlab).}
#'   \item{HAR}{HAR forecast of Corsi (2009).}
#'   \item{HARQ}{HARQ forecast of Bollerslev, Patton and Quaedvlieg
#'     (2016).}
#'   \item{ARFIMA}{Fractionally integrated AR forecast.}
#'   \item{vix_lag}{VIX close from the previous trading day, used as
#'     conditioning variable.}
#' }
#'
#' @source Replication package of Li, Liao and Quaedvlieg (2022),
#'   \url{https://zenodo.org/record/4884813}. Realized variance series
#'   adapted from Bollerslev, Patton and Quaedvlieg (2016), retrieved
#'   from Quaedvlieg's website. VIX from CBOE.
#'
#' @references
#' Bollerslev, T., Patton, A. J. and Quaedvlieg, R. (2016). Exploiting
#' the errors: a simple approach for improved volatility forecasting.
#' \emph{Journal of Econometrics}, 192(1), 1-18.
#'
#' Corsi, F. (2009). A simple approximate long-memory model of realized
#' volatility. \emph{Journal of Financial Econometrics}, 7(2), 174-196.
#'
#' Li, J., Liao, Z. and Quaedvlieg, R. (2022). Conditional Superior
#' Predictive Ability. \emph{Review of Economic Studies}, 89(2),
#' 843-875.
#'
#' @examples
#' data(llq2022)
#' head(llq2022)
#' # Squared-error losses per model:
#' losses <- (llq2022[, c("AR1","AR22","AR22_Lasso","HAR","HARQ","ARFIMA")] -
#'              llq2022$rv)^2
#' colMeans(losses)
"llq2022"