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All Journal ARRUS Journal of Engineering and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Annisa Syalsabila
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.
Measuring Systemic Risk of Indonesian State-Owned Banks Using CoVaR and Hybrid LASSO-QRNN Annisa Syalsabila
ARRUS Journal of Engineering and Technology Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech4941

Abstract

This study examines the systemic risk contributions of four major Indonesian state-owned banks, specifically BCA, BRI, Bank Mandiri, and BNI, to the Jakarta Composite Index (IHSG) using a Conditional Value-at-Risk (CoVaR) framework estimated through a Hybrid LASSO-QRNN approach. The study employs 2,491 daily trading observations from November 2016 to June 2026, sourced from Yahoo Finance, Bank Indonesia, and Investing.com, with out-of-sample evaluation on 375 observations from December 2024 to June 2026. Following the hybrid framework of Syalsabila et al. (2024), Step 1 applies LASSO-Quantile Regression (LASSO-QR) to select macroeconomic contagion amplifiers at quantiles q = 0.05 and q = 0.01, corresponding to 95% and 99% confidence levels respectively, while Step 2 trains a Quantile Regression Neural Network (QRNN) on the selected features to estimate conditional quantiles of system-level returns. The results reveal that BNI is the most systemically important institution, with mean ?CoVaR of -0.920% at 95% confidence and -1.991% at 99% confidence, followed by BRI, Bank Mandiri, and BCA. LASSO-QR retains all five macroeconomic variables in the system-level model, contrasting with the sparse two-to-five variable selection at the institution level. The findings further document BRI, Mandiri, and BNI each show a statistically significant attenuation of ?CoVaR during the 2026 domestic confidence crisisss three banks, consistent with the theoretical prediction that investor confidence deterioration compresses idiosyncratic institution-level risk contributions when market-wide confidence collapses.
Co-Authors Ikhwana, Nur Rahmanda, Lalu Ramzy Rais, Zulkifli Utomo, Agung Tri