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

作　　者：SHEN Tingting TAO Zhifu CHEN Huayou 

机构地区：[1]School of Economics,Anhui University,Hefei 230601,China [2]Center for Financial and Statistical Research,Anhui University,Hefei 230061,China [3]Stony Brook Institute at Anhui University,Anhui University,Hefei 230601,China [4]Center for Applied Mathematics,Anhui University,Hefei 230601,China

出　　处：《Journal of Systems Science & Complexity》2024年第2期759-775,共17页系统科学与复杂性学报（英文版）

基　　金：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;The teacher project of Anhui Ecology and Economic Development Research Center in 2021 under Grant No.AHST2021002.

摘　　要：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.

关 键 词：ARFIMAX-FIGARCH interval-valued time series IV-VARFIMA long-memory process WTI crude oil futures price 

分 类 号：O211.61[理学—概率论与数理统计]