BOOTSTRAP BASED MULTI-STEP AHEAD JOINT FORECAST DENSITIES FOR FINANCIAL INTERVAL-VALUED TIME SERIES

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Atıf İçin Kopyala

BEYAZTAŞ B. H.

COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS, cilt.70, sa.1, ss.156-179, 2021 (ESCI)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 70 Sayı: 1
• Basım Tarihi: 2021
• Doi Numarası: 10.31801/cfsuasmas.534711
• Dergi Adı: COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS
• Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
• Sayfa Sayıları: ss.156-179
• Anahtar Kelimeler: Multivariate forecast, resampling methods, interval-valued time series, vector autoregressive model, PREDICTION REGIONS, VECTOR, STATISTICS, CAUSALITY, PRICES, MODELS
• Dokuz Eylül Üniversitesi Adresli: Hayır

Özet

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.