印尼电信股份有限公司股票收益的时间序列模型。

Hana Rahma Trifanni, D. Permana, N. Amalita, A. A. Putra
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引用次数: 0

摘要

时间序列数据建模中的一种是ARMA模型,该模型假设波动率恒定。然而,在经济和金融数据中,有很多情况下波动性不是恒定的。这导致残差出现异方差问题,因此需要GARCH模型。除了异方差之外,残差的另一个问题是不对称效应或杠杆效应。为此,我们需要非对称GARCH建模。本研究旨在比较ARMA、GARCH和非对称GARCH模型的精度。本研究属于应用研究。使用的数据是2020年2月至2022年2月的每日股票收益数据多达488个数据。结果表明,ARMA(0,1)是股票收益波动率的最佳模型。该模型具有较好的精度,MAD值为0.0018644,RMSE值为0.0025352。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Modeling on Stock Return at PT. Telecommunication Indonesia Tbk.
      One of the time series data modeling is the ARMA model which assumes constant volatility. However, in economic and financial data, there are many cases where volatility is not constant. This results in the occurrence of heteroscedasticity problems in the residuals, so a GARCH model is needed. In addition to heteroscedasticity, another problem with residuals is the asymmetric effect or leverage effect. For that we need asymmetric GARCH modeling. This study aims to compare the accuracy of the ARMA, GARCH, and asymmetric GARCH models. This research is an applied research. The data used is daily stock return data from February 2020 to February 2022 as many as 488 data. The results showed that the best model in modeling stock return volatility is ARMA(0,1). The accuracy of this model is very good with MAD value of 0,0018644 and RMSE value of 0,0025352.
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