Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi
Khairiati, Alfi
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pendekatan Compound Poisson-Lognormal untuk Estimasi Kerugian Agregat dan Manajemen Modal Pada Asuransi Kendaraan Khairiati, Alfi; Pujiati, Sri
SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI Vol. 35 No. 3 (2025): Sainstech : Jurnal Penelitian dan Pengkajian Sains dan Teknologi
Publisher : Institut Sains dan Teknologi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37277/stch.v35i3.2410

Abstract

Pertumbuhan jumlah kendaraan bermotor meningkatkan kebutuhan akan asuransi sekaligus risiko klaim akibat kecelakaan, pencurian, maupun bencana alam. Hal ini menimbulkan tantangan bagi perusajaan asuransi dalam menjaga keseimbangan premi, klaim, dan modal berbasis risiko. Permasalahan utamanya adalah bagaimana memodelkan kerugian agregat tahunan secara akurat untuk mendukung penetapan premi, perencanaan Risk-Based Capital (RBC), dan strategi reasuransi. Penelitian ini menggunakan pendekatan Compound Poisson-Lognormal dengan estimasi parameter dari data historis. Simulasi Monte Carlo dilakukan untuk menghasilkan distribusi kerugian agregat dan menghitung ukuran risiko berupa nilai harapan, Value-at-Risk (VaR) dan Tail Value-at-Risk (TVaR) pada tingkat kepercayaan 95% dan 99%. Hasil menunjukkan rata-rata kerugian tahunan sebesar Rp. 4,89 miliar, dengan  Rp. 8,72 miliar dan    Rp. 9,53 miliar. Analisis skenario premi (20%, 30%, 40%) menegaskan bahwa target modal berbasis TVaR memberikan margin keamanan lebih baik. Evaluasi retensi reasuransi menemukan titik optimal ketika biaya premi setara dengan penghematan modal, sementara stress testing (+20% frekuensi atau +30% severitas) meningkatkan kebutuhan modal lebih dari 15%. Kerangka ini memberikan dasar kuantitatif bagi perusahaan asuransi dalam menetapkan premi, mengelola RBC, dan mengoptimalkan perlindungan reasuransi.  Kata kunci: Compound Poisson-Lognormal, RBC, Simulasi Monte Carlo, TVaR, VaR.  
GENERALIZED NESTED COPULA REGRESSION TO UNVEIL THE IMPACT OF EXCHANGE RATES AND NIKKEI 225 ON BANK MANDIRI STOCK PRICE Khairiati, Alfi; Budiarti, Retno; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1167-1184

Abstract

Fluctuations in exchange rates and foreign stock indices strongly influence domestic stock performance, particularly in the banking sector, which is highly sensitive to global economic dynamics. Traditional financial models often fail to capture the complex, non-linear dependencies between these variables, underscoring the need for more advanced approaches. This study examines the effectiveness of copula-based regression models in predicting Bank Mandiri’s (BMRI) stock price using exchange rates and the Nikkei 225 Index as predictors. Conventional regression methods, such as Linear Regression, cannot adequately capture nonlinear relationships and tail dependencies in financial time series. To address this, we compare Elliptical Copula, Symmetric Archimedean Copula, Asymmetric Archimedean Copula, and Generalized Nested Copula models. Results show that the Generalized Nested Copula Regression achieves the lowest Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Weighted MAPE (wMAPE), effectively modeling asymmetric and tail dependencies that are crucial in financial forecasting. While Elliptical Copula (t-Copula) also provides strong predictive accuracy, Archimedean copulas perform poorly, failing to improve upon linear regression. These findings highlight the importance of flexible statistical models in financial prediction, demonstrating that copula-based regression offers a superior alternative to traditional methods. Unlike prior research that often relied on simpler copula families or linear models, this study introduces a Generalized Nested Copula Regression in the context of the Indonesian banking sector, addressing a gap in emerging market literature. The study assumes correctly specified marginal distributions and a stable dependency structure, which may limit applicability under rapidly changing market conditions. Future work should consider dynamic copula structures and additional economic indicators to further enhance predictive accuracy.
Co-Authors Najib, Mohamad Khoirun Retno Budiarti Sri Pujiati