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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Publication Details

Journal
Computational and Mathematical Methods
Published
2025-01-01
DOI
https://doi.org/10.1155/cmm4/1087525
Citations
4
Primary Topic
Financial Risk and Volatility Modeling
Type
article
Field-Weighted Citation Impact
8.83
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article

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

Valeriana Lukitosari, Amirul Hakam, Suhud Wahyudi, Sentot Didik Surjanto et al.
4 citations
Computational and Mathematical Methods
Financial Risk and Volatility Modeling
8.83
article

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

Valeriana Lukitosari, Amirul Hakam, Suhud Wahyudi, Sentot Didik Surjanto, Eva O. Pristia
article en
4 citations

Abstract

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.

Computational and Mathematical MethodsVol. 2025(1)
Sepuluh Nopember Institute of Technology (ID)
Openalex Percentile: Top 3%
Financial Risk and Volatility Modeling
8.83
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