González-Rivera, GloriaLuo, YunRuiz Ortega, EstherUniversidad Carlos III de Madrid. Departamento de Estadística2019-10-212019-10-212019-10-152387-0303https://hdl.handle.net/10016/29054We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches. After fitting a possibly non-Gaussian bivariate 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&P500 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.engAtribución-NoComercial-SinDerivadas 3.0 EspañaBootstrapConstrainted RegressionCoverage RatesLogarithmic TransformationQml Estimationworking paperC01C22C53open accessDT/0000001732