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Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application

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Abstract

Long-memory process has been widely studied in classical financial time series analysis, which has merely been reported in the field of interval-valued financial time series. The aim of this paper is to explore long-memory process in the prediction of interval-valued time series (IvTS). To model the long-memory process, two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average (IV-VARFIMA) and ARFIMAX-FIGARCH were established. In the developed long-memory pattern, both of the short term and long-term influences contained in IvTS can be included. As an application of the proposed models, interval-valued form of WTI crude oil futures price series is predicted. Compared to current IvTS prediction models, IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.

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Authors and Affiliations

  1. School of Economics, Anhui University, Hefei, 230601, China

    Tingting Shen & Zhifu Tao

  2. Center for Financial and Statistical Research, Anhui University, Hefei, 230061, China

    Tingting Shen & Zhifu Tao

  3. Stony Brook Institute at Anhui University, Anhui University, Hefei, 230601, China

    Huayou Chen

  4. Center for Applied Mathematics, Anhui University, Hefei, 230601, China

    Huayou Chen

Authors
  1. Tingting Shen
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  2. Zhifu Tao
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  3. Huayou Chen
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Correspondence to Zhifu Tao.

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The authors declare that there are no conflicts of interest.

Additional information

This research was supported by the Humanities and Social Sciences Research Youth Project of the Ministry of Education of China under Grant No. 21YJCZH148, the Natural Science Foundation of Anhui Province under Grant Nos. 2108085MG239, 2108085QG290, 2008085QG334, and 2008085MG226, the National Natural Science Foundation of China under Grant Nos. 72001001, 71901001, and 72071001, the Provincial Natural Science Research Project of Anhui Colleges, China under Grant No. KJ2020A0004, and The teacher project of Anhui Ecology and Economic Development Research Center in 2021 under Grant No. AHST2021002. ⋄ This paper was recommended for publication by Editor YANG Cuihong.

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Shen, T., Tao, Z. & Chen, H. Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application. J Syst Sci Complex 37, 759–775 (2024). https://doi.org/10.1007/s11424-024-2112-9

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  • DOI: https://doi.org/10.1007/s11424-024-2112-9

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