利用Glosten, Jaggnathan和Runkle (GJR)模型估计Sharia股份指数的风险均值(AVaR)

Proceedings of the 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020) Pub Date : 2021-05-11 DOI:10.2991/ASSEHR.K.210508.056

Sri Muslihah Bakhtiar, Ermawati, Ilham Syata, Wahidah Alwi, Risnawati Ibnas, Sri Dewi Anugrawati

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引用次数: 3

摘要

风险平均价值(AVaR)是一种衡量工具，用于评估投资者在一定时间内对证券投资经历的最大损失。此外。AVaR的信心水平需要满足所有关于风险投资者风险性质的公理。这是因为可以通过使用Glosten来估计损失风险来克服不对称波动响应的可能性。Jagganathan。和朗克尔(GJR)模型。在这项研究中。该调查采用了2018年1月1日至12月28日期间的股价数据。因此。本研究旨在利用Glosten Jagganathan和Runkle模型的平均风险值来确定股票价格损失的风险估计。结果表明，在95%置信水平为0.1627%的AVaR估计中得到的股票价格可能提前一天经历。

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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Estimation of Average Value at Risk (AVaR) on Sharia Joint-Stock Index Using Glosten, Jaggnathan and Runkle (GJR) model

The average value at risk (AVaR) is a measuring tool used to assess the worst loss experienced by an investor on a portfolio investment at a certain time. Furthermore. AVaR’s level of confidence needs to fulfill all the axioms regarding the nature of risk for risk-varse investors. This is because the possibility of an asymmetric volatility response can be overcomed by estimating the risk of loss using the Glosten. Jagganathan. and Runkle (GJR) models. In this study. the stock price data for the period January 1-28 December 2018 were used for the response. Therefore. this study aims to determine the risk estimation of stock price loss using the Average Value at Risk with the Glosten Jagganathan and Runkle models. The results showed that the stock price obtained from the AVaR estimation with a 95% confidence level of 0.1627% may be experienced one day ahead.

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Proceedings of the 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020)

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