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Prediction regions for interval‐valued time series
Author(s) -
GonzalezRivera Gloria,
Luo Yun,
Ruiz Esther
Publication year - 2020
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2754
Subject(s) - bivariate analysis , econometrics , volatility (finance) , range (aeronautics) , series (stratigraphy) , probabilistic logic , autoregressive model , interval (graph theory) , monte carlo method , point estimation , computer science , gaussian , profitability index , stochastic volatility , mathematics , statistics , economics , finance , paleontology , materials science , physics , biology , combinatorics , quantum mechanics , composite material
Summary 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.