Prediction regions for interval‐valued time series

Gloria Gonzalez‐Rivera, Yun Luo and Esther Ruiz ()

Journal of Applied Econometrics, 2020, vol. 35, issue 4, 373-390

Abstract: 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.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1002/jae.2754

Related works:
Working Paper: Prediction Regions for Interval-valued Time Series (2019)
Working Paper: Prediction Regions for Interval-valued Time Series (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:35:y:2020:i:4:p:373-390

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().