Prediction regions for interval¿valued time series Articles uri icon

publication date

  • June 2020

start page

  • 373

end page

  • 390


  • 4


  • 35

International Standard Serial Number (ISSN)

  • 0883-7252

Electronic International Standard Serial Number (EISSN)

  • 1099-1255


  • We approximate probabilistic forecasts for interval‐valued time series by offering alternative approaches. After fitting a possibly non‐Gaussian bivariate vector autoregression (VAR) model to the center/log‐range system, we transform prediction regions (analytical and bootstrap) for this system into regions for center/range and upper/lower bounds systems. Monte Carlo simulations show that bootstrap methods are preferred according to several new metrics. For daily S&P 500 low/high returns, we build joint conditional prediction regions of the return level and volatility. We illustrate the usefulness of obtaining bootstrap forecasts regions for low/high returns by developing a trading strategy and showing its profitability when compared to using point forecasts.