RT Generic
T1 Prediction regions for interval-valued time series
A1 González-Rivera, Gloria
A1 Luo, Yun
A1 Ruiz Ortega, Esther
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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.
SN 2387-0303
YR 2019
FD 2019-10-15
LK https://hdl.handle.net/10016/29054
UL https://hdl.handle.net/10016/29054
LA eng
NO Gloria González-Rivera acknowledgesfinancial support from the 2015/2016 Chair of Excellence UC3M/Banco de Santander andthe UC-Riverside Academic Senate grants. Esther Ruiz and Gloria González-Rivera are grateful to theSpanish Government contract grant ECO2015-70331-C2-2-R (MINECO/FEDER).
DS e-Archivo
RD 27 jul. 2024