Editor:
Universidad Carlos III de Madrid. Departamento de Estadística
Issued date:
2019-10-15
ISSN:
2387-0303
xmlui.dri2xhtml.METS-1.0.item-contributor-funder:
European Commission Ministerio de Economía y Competitividad (España)
Sponsor:
Gloria González-Rivera acknowledges
financial support from the 2015/2016 Chair of Excellence UC3M/Banco de Santander and
the UC-Riverside Academic Senate grants. Esther Ruiz and Gloria González-Rivera are grateful to the
Spanish Government contract grant ECO2015-70331-C2-2-R (MINECO/FEDER).
Serie/No.:
Working paper. Statistics and Econometrics 19-15
Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
Abstract:
We 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 We 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.[+][-]