Shindi Shella May Wara

University of Pembangunan Nasional Veteran Jawa Timur

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Muhammad Nasrudin

University of Pembangunan Nasional Veteran Jawa Timur

Andri Fauzan Adziima

University of Pembangunan Nasional Veteran Jawa Timur

Andri Fauzan Adziima

University of Pembangunan Nasional Veteran Jawa Timur

Dedy Dwi Prastyo

Sepuluh Nopember Institute of Technology

Alfan Rizaldy Pratama

University of Pembangunan Nasional Veteran Jawa Timur

Heri Kuswanto

Sepuluh Nopember Institute of Technology

Arista Pratama

University of Pembangunan Nasional Veteran Jawa Timur

Muhammad Nasrudin

University of Pembangunan Nasional Veteran Jawa Timur

Endah Setyowati

University of Brawijaya

Cooperative Institutions

University of Pembangunan Nasional Veteran Jawa Timur

Sepuluh Nopember Institute of Technology

University of Technology Malaysia

Institut Bisnis dan Informatika Kesatuan

Universitas Informatika dan Bisnis Indonesia

Lambung Mangkurat University

UIN Raden Mas Said Surakarta

Universitas Islam Jember

Universitas Cokroaminoto Yogyakarta

Universitas Wijaya Kusuma Surabaya

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Value at risk estimation with hybrid-SVR-GARCH-KDE model for LQ45 portfolio optimization

AIP conference proceedings (2023)

Shindi Shella May Wara Dedy Dwi Prastyo Heri Kuswanto

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Shindi Shella May Wara, Dedy Dwi Prastyo, Heri Kuswanto; Value at risk estimation with hybrid-SVR-GARCH-KDE model for LQ45 portfolio optimization. AIP Conference Proceedings 27 January 2023; 2540 (1): 080013. https://doi.org/10.1063/5.0107539 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

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10.1063/5.0107539

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Citations (7)

Optimizing Urban Farming Potential Using Google Street View and Machine Learning

Andri Fauzan Adziima Shindi Shella May Wara Arista Pratama Muhammad Nasrudin

10.1109/itis64716.2024.10845695

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Predictive Analysis of Government Application Comment on Playstore with Clustered Support Vector Machine

Shindi Shella May Wara Andri Fauzan Adziima Muhammad Nasrudin Alfan Rizaldy Pratama

10.1109/itis64716.2024.10845453

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Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector

Jurnal Varian (2024)

Muhammad Nasrudin Endah Setyowati Shindi Shella May Wara

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.

Stock (firearms)

Vector autoregression

10.30812/varian.v8i1.4239

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Citations (1)