Gloria González-Rivera
Yun Luo
Esther Ruiz

prediction regions for interval‐valued time series (replication data)

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.

Data and Resources

Suggested Citation

González-Rivera, Gloria; Luo, Yun; Ruiz, Esther (2020): Prediction regions for interval‐valued time series (replication data). Version: 1. Journal of Applied Econometrics. Dataset.