Prediction Regions for Interval-valued Time Series

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

No 201921, Working Papers from University of California at Riverside, Department of Economics

Abstract: We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches. After fi tting 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 pro fitability when compared to using point forecasts.

Keywords: Bootstrap; Constrainted Regression; Coverage Rates; Logarithmic Transformation; QML estimation. (search for similar items in EconPapers)
JEL-codes: C01 C22 C53 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-for and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/201921.pdf First version, 2019 (application/pdf)

Related works:
Journal Article: Prediction regions for interval‐valued time series (2020)
Working Paper: Prediction regions for interval-valued time series (2019)
Working Paper: Prediction Regions for Interval-valued Time Series (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201921

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