Autocorrelation Regression Model Analysis and Selection of Cross-Border RMB Settlement From 2011 to 2020

Cheng Zhang, Nianjia Hu, Qiang Yan
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Abstract

With China's continuous opening to the outside world, changes in the international environment and the operation of the cross-border RMB settlement system (CIPS), the scale of cross-border RMB settlement has fluctuated continuously. In response to this phenomenon, the authors collected and sorted out the total amount of RMB cross-border settlement and payments from 2011 to 2020 time sequence data in China, then use five AR models including ARMA, GARCH(1.1), EGARCH(1.1), PARCH(1.1), and CARCH(1.1) to fit. The experimental results show that the four autocorrelation models all prove that the cross-border RMB settlement has autocorrelation relationship, and the long-term trend continues to grow up. According to the precision and accuracy of the five models, the ARMA model equation is one optimal prediction equation. On the basis of the ARMA model equation, and the establishment of the VENSIM system dynamics simulation model, the scale of China's cross-border RMB settlement in the next 10 years is predicted.
2011 - 2020年跨境人民币结算的自相关回归模型分析与选择
随着中国对外开放的不断扩大,国际环境的变化以及跨境人民币结算系统(CIPS)的运行,跨境人民币结算规模不断波动。针对这一现象,笔者收集整理了2011 - 2020年中国人民币跨境结算支付总额时间序列数据,并采用ARMA、GARCH(1.1)、EGARCH(1.1)、PARCH(1.1)、CARCH(1.1)五个AR模型进行拟合。实验结果表明,四种自相关模型均证明人民币跨境结算存在自相关关系,且长期趋势持续增长。从五种模型的精度和准确度来看,ARMA模型方程是一种最优预测方程。在ARMA模型方程的基础上,建立VENSIM系统动力学仿真模型,对未来10年中国跨境人民币结算规模进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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