Prediction regions for interval-valued time series

Gloria Gonzalez-Rivera and Yun Luo

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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 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.

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: 2019-10-15
New Economics Papers: this item is included in nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:29054

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