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

The Indonesian composite stock price index is an indicator of changes in stock prices as a guide for investors to invest in reducing risk. The regression model for Indonesian Composite Index (ICI) has the response variable as stock prices with fluctuation behavior and several financial predictor variables, the model tends to violate the assumptions of normality, homoscedasticity, autocorrelation and multicollinearity. This problem can be overcome by modeling the composite stock price index by using the Artificial Neural Network (ANN) and nonparametric regression of Multivariate Adaptive Regression Spline (MARS). In this study, the time series data from the composite stock price index starting from April 2003 to March 2018 with its predictor variables are crude-oil prices, interest rates, inflation, exchange rates, gold prices, Dow Jones price, and Nikkei 225 Index. The both methods give better goodness of fit, where the coefficient of determination ANN is 0.98925 and the MARS determination coefficient is 0.99427. While based on the Mean Absolute Percentage Error (MAPE) of ANN was obtained 6.16383 and the MAPE value of MARS is 4.51372. This means that the ANN method and nonparametric MARS regression method have good performance to forecast the value of the Indonesian composite stock price index in the future, but in this case of data the nonparametric MARS regression method shows the accuracy of the model is slightly better than ANN.

The stock exchange becomes one of indicator shown the modernity of one country and investment alternative as well. Stock Exchange activity reflected by an Indonesia Composite Index fluctuation which are influenced by macroeconomics variables such as interest rate, exchange value, and inflation. This study aims to determine the effect of, interest rates, exchange rate of rupiah and inflation against the Indonesia Composite Index(ICI. The type of research used explanatory research, with quantitative approach and took all time series data covering Interest Rates, Rupiah Exchange Rate, Inflation and Indonesia Composite Index(ICI) for the period of 1996 until 2020. The data analysis used multiple linear regression analysis using SPSS 23. The results of this studyindicate that the value of coefficient of determination(R2) 72.9% which means independent variables affect the dependent variable 72.9% and the rest is 27.1% influenced by other variables outside this study. F test results indicate that the independent variables of interest rate, exchange rate of rupiah, and inlation rate simultaneously have significant effect on the Indonesia Composite Index(ICI). The result of t test shows that the, interest rate variable partially have significant negative effect on Indonesia Composite Index(ICI), exchange rate variable partially have significant positive effeft on Indonesia Composite Index, while inflation variable partially negatively influenced the Indonesia Composite Index(ICI).

The stock exchange becomes one of indicator shown the modernity of one country and investment alternative as well. Stock Exchange activity reflected by an Indonesia Composite Index fluctuation which are influenced by macroeconomics variables such as interest rate, exchange value, and inflation. This study aims to determine the effect of, interest rates, exchange rate of rupiah and inflation against the Indonesia Composite Index(ICI. The type of research used explanatory research, with quantitative approach and took all time series data covering Interest Rates, Rupiah Exchange Rate, Inflation and Indonesia Composite Index(ICI) for the period of 1999 until 2023. The data analysis used multiple linear regression analysis using SPSS 23. The results of this study indicate that the value of coefficient of determination(R2) 86,5% which means independent variables affect the dependent variable 86,5% and the rest is 14,5% influenced by other variables outside this study. F test results indicate that the independent variables of interest rate, exchange rate of rupiah, and inlation rate simultaneously have significant effect on the Indonesia Composite Index(ICI). The result of t test shows that the, interest rate variable partially have significant negative effect on Indonesia Composite Index(ICI), inflation variable partially positively influenced the Indonesia Composite Index(ICI), while exchange rate variable partially have significant positive effeft on Indonesia Composite Index (ICI).

The stock exchange becomes one of indicator shown the modernity of one country and investment alternative as well. Stock Exchange activity reflected by an Indonesia Composite Index fluctuation which are influenced by macroeconomics variables such as interest rate, exchange value, and inflation. This study aims to determine the effect of, interest rates, exchange rate of rupiah and inflation against the Indonesia Composite Index(ICI. The type of research used explanatory research, with quantitative approach and took all time series data covering Interest Rates, Rupiah Exchange Rate, Inflation and Indonesia Composite Index(ICI) for the period of 1996 until 2020. The data analysis used multiple linear regression analysis using SPSS 23. The results of this studyindicate that the value of coefficient of determination(R2) 72.9% which means independent variables affect the dependent variable 72.9% and the rest is 27.1% influenced by other variables outside this study. F test results indicate that the independent variables of interest rate, exchange rate of rupiah, and inlation rate simultaneously have significant effect on the Indonesia Composite Index(ICI). The result of t test shows that the, interest rate variable partially have significant negative effect on Indonesia Composite Index(ICI), exchange rate variable partially have significant positive effeft on Indonesia Composite Index, while inflation variable partially negatively influenced the Indonesia Composite Index(ICI).

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH-MIDAS model improved its forecasting ability, however, in Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCH-MIDAS model.

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH MIDAS model improved its forecasting ability, however, for Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCHMIDAS model.

Macroeconomic Impact on Stock Market Returns and Volatility: Evidence from Sri Lanka

Research aims: This study aims to investigate empirical evidence of a comparison of investment instruments, including Bitcoin, Indonesia Composite Index (ICI), and gold, before and during the COVID-19 pandemic.Design/Methodology/Approach: Analytical methods employed comparison study using secondary data. The Microsoft Excel program was utilized to calculate formulas for each variable. Then, data were statistically processed using the SPSS application, i.e., independent sample test, testing for differences. The sample used in this study was the closing price of Bitcoin, the price of the ICI, and the price of gold, with monthly data from the beginning of January 2018 to the end of December 2021, to demonstrate a significant difference between the risks of Bitcoin, ICI, and gold before and during the COVID-19 pandemic.Research findings: The hypothesis test results revealed that before the COVID-19 pandemic, the investment risk of ICI and gold was the lowest, with a significance level of 0.000 (0.0000.05) on a different t-test at a 5% significance level. Thus, there was a significant difference in investment risk between ICI and gold. Meanwhile, during the COVID-19 pandemic, the risk of investing between Bitcoin and the ICI was the lowest, with a significance level of risk of was 0.000 (0.000 0.05) on different tests at the significance levels of 5%. In short, there was a big difference in investment risk between Bitcoin and the ICI.Theoretical contribution/Originality: This study provides additional literature on decision-making, especially on risk.

Measuring risk is the key component in many asset pricing models. Although volatility is the most widely used measure for the risk, Value at Risk (VaR) and Maximum drawdown (MDD) are also considered as alternative risk measure. This article questions whether VaR and MDD contain additional information to volatility in equity market. The empirical analysis is conducted using the stocks listed in Korean stock market. By constructing portfolios in accordance with three risk measures, cross-sectional predictability is tested. The primary findings are as follow; (1) the return patterns are bell shaped in all measures and (2) VaR and MDD do not capture additional risk factors after conditioning volatility.

The Impact of Macroeconomic Variables on Stock Market Volatility: Evidence from Cross-Country Analysis Pre, During and Post GFC

Purpose - This study investigates whether the volatility of stock market returns is determined by macroeconomic variables either as individual or as a group, within the context of Vietnam – a frontier emerging market. Six macroeconomic factors have been selected, including economic growth (GDP), consumer price index (CPI), broad money supply (M2), interest rate (represented by refinancing rate – FR), foreign exchange rate USD/VND (EX), and foreign direct investment (FDI). Methodology - Using 161 monthly observations collected from August 2000 to December 2013, the paper employs general autoregressive conditional heteroskedasticity (GARCH) framework to measure stock market volatility as well as to estimate this volatility under indicated macroeconomic impacts. Findings - Taking the volatility clustering into account, the GARCH (1,1) models reveal that the volatility of Vietnam’s stock market returns is highly persistent, suggesting a long memory of the volatility in response of a shock. Additionally, the stock market volatility could be predicted better using previous shocks (i.e. those originating from GDP, CPI and EX) rather than the previous volatility itself. Conclusion - The prediction of Vietnam’s stock market volatility could be better based on the selected macroeconomic indicators. A monthly change in consumer price index appears as the most essential indicator that help predicting the volatility of the Vietnam’s stock market. Any news about economic growth can be considered as the second significant factor in explaining Vietnam stock return volatility. Furthermore, the univariate analysis shows a statistical significant evidence for the impact of a change in the exchange rate (USD/VNA) on Vietnam’s stock market volatility.

The Indonesian Composite Index (ICI) reflects Indonesia's economic growth. ICI predictions are significant considerations for investors when making investment decisions. Two approaches can be used to predict ICI: parametric and nonparametric approaches. Therefore, this study compares parametric and nonparametric approaches to predict ICI. In its application, the parametric approach requires several assumptions to be met, such as linearity. This differs from analysis with a nonparametric approach that does not require certain assumptions. The parametric approach in this study uses the ARIMA model. ARIMA is widely used to predict time series data. In the nonparametric approach, in this study, we used nonparametric regression based on the least square spline. Spline is chosen because it can handle data that tends to fluctuate by placing knot points when data changes occur. In this study, ICI monthly data obtained from the website investing.com was used. Investing.com is a website that financial analysts often use as a data source to monitor world economic conditions, including the ICI. The Mean Absolute Percentage Error (MAPE) value is determined to assess the accuracy of the prediction. The study results indicate that the analysis with ARIMA cannot meet the assumptions, so ARIMA modeling cannot be continued. Different results were obtained in nonparametric regression modeling based on the least square spline estimator. Prediction of ICI using nonparametric regression based on the least square spline estimator has a MAPE value of 2.613% (less than 10%), which means the model is a highly accurate prediction, meaning modeling using nonparametric regression based on the least square spline estimator is better than the ARIMA model for predicting ICI.

Value at Risk (VaR) is one of the tools recommended Bank Indonesia to gauge the risk of an investment, the VaR approach tends to be more associated with the conventional assumption of a normal distribution, while contemporary empirical findings indicate the existence of patterns of abnormality in the nature of statistical data, especially on financial data. Up to this time shares in the Jakarta Islamic Index (JII) is still heavily influenced by the dynamics of market volatility which one, so the necessary in-depth analysis to help investors make the right decisions in investing. This research addresses the issue of risk analysis model using the VaR approach using a variety of model Heterokedastic Timeseris Conditionals (CHT) and find the best model. As for the data used is the daily closing stock index data-Sharia stocks (JII) post-crisis global 2008 (January 2009 – June 2011) and the software used is E-Views 5.1 and Excel 2007. The results obtained are of 16 (sixteen) model approach to VaR-CHT used, only 5 (five) a valid model on a confidence level of 99%, i.e. Approach (2.2) GARCH, GARCH M standard deviation GARCH (1,1), M Log (Variansi) (1,1), TARCH M Log (Variansi) (1,1), EGARCH and M Log (Variansi) (1,1). The VaR Model of the CHT are the best and recommended in analyzing the risks of stock investment is Shariah (JII) is a model that gives the value of the VaR model, i.e. the smallest VaR GARCH-M standard deviation (1,1) that gives the value of VaR is equal to 3.2396%.

This study aims to evaluate and compare the investment risk levels of three primary financial instruments in Indonesia: the Indonesia Composite Index (IHSG), Bank Central Asia (BCA) with code BBCA stock, and gold. Using the Value at Risk (VaR) approach based on Monte Carlo simulation, the research integrates these assets into a unified risk assessment model, providing a more comprehensive perspective than prior studies that typically examine individual assets or use historical estimation methods. Monthly historical price data from April 2024 to March 2025 were obtained from the Indonesia Stock Exchange and the Central Bureau of Statistics. The analysis involved calculating asset returns, means, and standard deviations, followed by 10,000 Monte Carlo simulation iterations to generate potential future price paths and estimate losses under market uncertainty. The results, at a 95% confidence level, show that IHSG carries the highest investment risk, with a VaR of IDR 9,331.16, followed closely by BBCA stock at IDR 9,210.31. Gold exhibited the lowest risk level, with a VaR of IDR 6,896.86, confirming its role as a more stable and less volatile investment compared to equity-based assets. These findings highlight the reliability of gold as a defensive asset during turbulent market conditions. The application of Monte Carlo simulation effectively captures the non-normal distribution of returns and accommodates complex market behaviors, making it a robust tool for financial risk modeling. This research offers meaningful insights for investors, analysts, and academics in optimizing portfolio strategies and improving risk management decisions.

Composite Stock Price Index (CSPI) can be used as a reflection of the national economic condition of a country because it is an indicator to know the development the capital market in a country. Therefore, the movement in the future needs to be forecast. This study aims to build a model for the time series forecasting of Indonesia Composite Index (ICI) using the ARIMA model. The data used is the monthly data of ICI in Indonesia Stock Exchange (IDX) from January 2000 until December 2017 as many as 216 data. The method used in this research is the Box-Jenkins method. The autocorrelation (ACF) and partial autocorrelation function (PACF) are used for stationary test and model identification. The maximum estimated likelihood is used to estimate the parameter model. In addition, to select a model then used Akaike’s Information Criterion (AIC). Ljung-Box Q statistics are used for diagnostic tests. In addition, to show the accuracy of the model, we use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and the most appropriate model is ARIMA (0, 1, 1).