风险股票市场短期和长期波动趋势的估计

IF 0.9 Q3 MATHEMATICS, APPLIED
Valeriana Lukitosari, Eva O. Pristia, Sentot D. Surjanto, Amirul Hakam, Suhud Wahyudi
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引用次数: 0

摘要

股票市场的波动是常见的。投资者因其投资活动可能招致的损失称为投资风险。由于各种情况,投资回报可能达不到预期。模型与数据的拟合;表现波动性、预测、稳定性和分析的能力;口译目标都是需要考虑的因素。本研究利用GARCH-MIDAS模型,结合印尼银行利率(BIIR)、消费者价格指数(CPI)、有效联邦基金利率(EFFR)和通货膨胀率(IR)等月度宏观经济指标,研究了印尼综合指数(ICI)的波动性。我们首先通过图形分析ICI回报和宏观经济变量的趋势,以确定潜在的模式和变化。描述性统计提供了详细的数字总结,为深入的实证分析奠定了基础。使用GARCH-MIDAS模型估计股票市场波动的长期组成部分,其中包括宏观经济变量,以捕捉它们对市场波动的影响。采用极大似然估计(MLE)来估计模型参数,以确保对观测数据的鲁棒拟合。我们的研究结果表明,与以往的研究相反,EFFR对ICI波动的影响最为显著。使用均方误差(MSE)和平均绝对误差(MAE)评估预测性能,确认EFFR变量具有优越的预测能力。该研究使用ICI的风险值(VaR)来评估风险,并结合EFFR来考虑宏观经济对市场波动的影响。99%和95%置信水平的VaR值提供了对潜在最大损失的洞察,有助于明智的投资决策。本研究增强了对宏观经济变量与股票市场波动之间关系的认识,为投资者和政策制定者在印尼股票市场进行风险管理和投资策略优化提供了重要信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimating the Trends of Volatility in the Risk Equity Market Over the Short and Long Terms

Estimating the Trends of Volatility in the Risk Equity Market Over the Short and Long Terms

Market fluctuations in the stock sector are common. The possible loss that investors may incur because of their investment activity is referred to as investment risk. Returns on investments may fall short of expectations due to a variety of circumstances. Fit of the model to the data; performance in representing volatility, prediction, stability, and analysis; and interpretation goals are all factors to consider. This study investigates the volatility of the Indonesian composite index (ICI) using the GARCH-MIDAS model, integrating daily ICI returns with monthly macroeconomic indicators: Indonesian bank interest rates (BIIR), consumer price index (CPI), effective federal fund rate (EFFR), and inflation rate (IR). We begin by graphically analysing the trends in ICI returns and macroeconomic variables to identify potential patterns and shifts. Descriptive statistics offer a detailed numerical summary, setting the stage for in-depth empirical analysis. The long-run component of stock market volatility is estimated using the GARCH-MIDAS model, with macroeconomic variables included to capture their impact on market fluctuations. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, ensuring a robust fit to the observed data. Our findings indicate that the EFFR has the most significant impact on ICI volatility, contrary to previous studies. Forecasting performance is evaluated using mean squared error (MSE) and mean absolute error (MAE), confirming the superior predictive capability of the EFFR variable. The study assesses risk using value at risk (VaR) for the ICI, incorporating the EFFR to account for macroeconomic influences on market volatility. VaR values at 99% and 95% confidence levels provide insights into potential maximum losses, aiding in informed investment decision-making. This research enhances knowledge of the relationship between macroeconomic variables and stock market volatility, providing investors and policymakers with important information for risk management and investment strategy optimization in the Indonesian equity market.

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