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May Wara, Shindi Shella
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Public Health Social Sciences Other

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Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector Nasrudin, Muhammad; Setyowati, Endah; May Wara, Shindi Shella
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4239

Abstract

Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates thepresence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with theGARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)-GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stockprices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCHmodel could successfully handle the volatility of stock price data. In general, this research is in linewith previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatilitypatterns in the data.
Klasterisasi Produktivitas Daerah di Jawa Tengah Berdasarkan Ketenagakerjaan Menggunakan K-Means dan Average Linkage Nashrullah, Ahmad Firqi; Mahardhika, Rivaldi Dwi; Rusdiyanto, Nur Rahmat; May Wara, Shindi Shella; Saputra, Wahyu Syaifullah Jauharis
JURNAL DIFERENSIAL Vol 7 No 2 (2025): November 2025
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v7i2.22516

Abstract

This study employs K-Means and Agglomerative Clustering (Average Linkage) to group regions based on variables such as the number of residents, unemployment rate, and other supporting indicators. The data are normalized and evaluated using the Silhouette Score metric, yielding three optimal clusters. Average Linkage (0.3596) outperforms K-Means (0.2627). The Average Linkage results indicate that cluster 1 is characterized by stable productivity and low unemployment, cluster 2 consists solely of Semarang City with the highest Human Development Index and wages, and cluster 3 comprises underdeveloped areas with high unemployment and low wages. This clustering is highly beneficial for supporting more targeted data-driven regional development policies.
Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector Nasrudin, Muhammad; Setyowati, Endah; May Wara, Shindi Shella
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4239

Abstract

Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates thepresence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with theGARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)-GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stockprices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCHmodel could successfully handle the volatility of stock price data. In general, this research is in linewith previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatilitypatterns in the data.
Co-Authors AA Sudharmawan, AA Endah Setyowati Mahardhika, Rivaldi Dwi Muhammad Nasrudin Nashrullah, Ahmad Firqi Rusdiyanto, Nur Rahmat Saputra, Wahyu Syaifullah Jauharis