Bootstrap based multi-step ahead joint forecast densities for financial interval-valued time series

Year 2021, Volume: 70 Issue: 1, 156 - 179, 30.06.2021

Beste Hamiye Beyaztaş

https://doi.org/10.31801/cfsuasmas.534711

Cited By: 1

Abstract

This study presents two interval-valued time series approaches to construct multivariate multi-step ahead joint forecast regions based on two bootstrap algorithms. The first approach is based on fitting a dynamic bivariate system via a VAR process for minimum and maximum of the interval while the second approach applies for mid-points and half-ranges of interval-valued time series. As a novel perspective, we adopt two bootstrap techniques into the proposed interval-valued time series approaches to obtain joint forecast regions of the lower/upper bounds of the intervals. The forecasting performances of the proposed approaches are evaluated by extensive Monte Carlo simulations and two real-world examples: (i) monthly S&P 500 stock indices; (ii) monthly USD/SEK exchange rates. Our results demonstrate that the proposed approaches are capable of producing valid multivariate forecast regions for interval-valued time series.

Keywords

Multivariate forecast, resampling methods, interval-valued time series

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