Lumbantobing, Elfa Eukaristia Theresia (2025) Prediksi Return Saham Pertambangan Dengan Metode ARIMA-GARCH Dan Perhitungan Value At Risk Menggunakan Pendekatan Copula. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Investasi di pasar modal semakin diminati di Indonesia, sebagaimana tercermin dari peningkatan jumlah investor yang tercatat oleh Otoritas Jasa Keuangan (OJK) pada tahun 2023. Salah satu sektor yang berkontribusi besar terhadap perekonomian nasional adalah sektor pertambangan, yang berperan penting dalam memenuhi kebutuhan energi dan bahan baku industri. Di antara saham unggulan di sektor ini, PT Adaro Energy Indonesia Tbk (ADRO) dan PT Aneka Tambang Tbk (ANTM) termasuk dalam indeks LQ45, yang mencakup saham-saham dengan likuiditas tinggi dan kapitalisasi pasar terbesar di Bursa Efek Indonesia (BEI). Penelitian ini bertujuan untuk memprediksi return beserta volatilitas saham ADRO dan ANTM menggunakan model ARIMA-GARCH serta menganalisis hubungan dependensi antara kedua saham tersebut dengan pendekatan Copula Archimedean. Data yang digunakan merupakan data closing price harian saham yaitu ADRO dan ANTM selama periode 1 Januari 2022 hingga 31 Desember 2024. Model ARIMA-GARCH menangkap pola pergerakan harga saham dan volatilitas heteroskedastik, lalu residualnya dianalisis dengan Copula Frank, Gumbel, dan Clayton untuk memilih dependensi terbaik berdasarkan log-likelihood terbesar. Pemilihan Copula Archimedean dalam penelitian ini didasarkan pada hasil uji distribusi normal yang menunjukkan bahwa kedua residual return saham tidak berdistribusi normal sehingga mampu menangkap struktur dependensi yang fleksibel, terutama dalam menangani hubungan asimetris dan ekor ketebalan pada distribusi return saham. Copula terbaik digunakan dalam simulasi Monte Carlo untuk menghitung Value at Risk (VaR) portofolio saham sebagai ukuran risiko investasi. Berdasarkan hasil penelitian, model ARIMA (1, 0, [6])-GARCH (1, 1) adalah model terbaik untuk saham ADRO serta model ARIMA ([18], 0, [18])-GARCH (1, 1) adalah model terbaik untuk saham ANTM. Secara keseluruhan, model ARIMA-GARCH cukup baik dalam memprediksi return dan volatilitas saham. Dalam analisis dependensi, diperoleh koefisien Kendall’s Tau sebesar 0,19123 dan estimasi parameter Copula Archimedean sebesar 1,79038 (Copula Frank); 1,19690 (Copula Gumbel); dan 0,47288 (Copula Clayton). Estimasi VaR portofolio saham dilakukan dengan menggunakan input parameter Copula terbaik yaitu Copula Frank karena memiliki nilai log-likelihood terbesar di antara Copula lainnya. Hasil estimasi VaR portofolio saham pada tingkat kepercayaan 90% sebesar -0,02231, tingkat kepercayaan 95% sebesar -0,02950, serta tingkat kepercayaan 99% sebesar -0,04635. Hasil analisis ini diharapkan dapat menjadi gambaran bagi perusahaan maupun perorangan dalam mengambil keputusan investasi yang lebih tepat di sektor pertambangan.
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Investment in the capital market has been increasingly favored in Indonesia, as reflected by the rising number of investors recorded by the Financial Services Authority (OJK) in 2023. One sectors that significantly contributes to the national economy is the mining sector, which plays
an important role in meeting the energy and industrial raw material needs. Among the leading stocks in this sector, PT Adaro Energy Indonesia Tbk (ADRO) and PT Aneka Tambang Tbk
(ANTM) are included in the LQ45 index, which consists of highly liquid stocks with the largest market capitalization on the Indonesia Stock Exchange (BEI). This study aims to predict the returns and volatility of ADRO and ANTM stocks using ARIMA-GARCH model and to analyze the dependency relationship between the two stocks using the Archimedean Copula approach. The data used are daily closing price of ADRO and ANTM stocks over the period from January 1, 2022, to December 31, 2024. The ARIMA-GARCH model captures the stock price movement patterns and heteroskedastic volatility, then the residuals are analyzed with Frank, Gumbel, and Clayton Copulas to select the best dependency based on the highest log�likelihood value. The selection of Archimedean Copula in this study is based on the normality
test results, which shows that the residual returns of both stocks are not normally distributed. Therefore, this approach can capture a flexible dependency structure, especially in handling asymmetric relationships and heavy tails in stock return distributions. The best Copula is used
in Monte Carlo simulation to calculate the portofolio’s Value at Risk (VaR) as a measure of investment risk. Based on the results, the ARIMA (1, 0, [6])-GARCH (1, 1) model is the best fit for ADRO stock, while the ARIMA ([18], 0, [18])-GARCH (1, 1) model is the best for ANTM stock. Overall, the ARIMA-GARCH model performs well in predicting stock returns and volatility. In dependency analysis, the Kendall’s Tau coefficient is 0,19123, and the Archimedean Copula parameter estimates are 1,79038 (Frank Copula); 1,19690 (Gumbel Copula); and 0,47288 (Clayton Copula). The portfolio VaR estimation uses the input parameter of the best Copula, which is the Frank Copula, as it has the highest log-likelihood value among the Copulas. The estimated VaR of the stock portfolio at confidence levels of 90%, 95%, and 99% are -0,02231; -0,02950; and -0,04635. These results are expected to provide valuable insights for companies and individual investors in making more precise investment decisions in the mining sector.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | ADRO, ANTM, ARIMA-GARCH, Copula Archimedean, Simulasi Monte Carlo, VaR, ADRO, ANTM, Archimedean Copula, ARIMA-GARCH, Monte Carlo Simulation, VaR |
| Subjects: | H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. H Social Sciences > HG Finance > HG4915 Stocks--Prices |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis |
| Depositing User: | Elfa Eukaristia Theresia Lumbantobing |
| Date Deposited: | 14 Jul 2025 02:25 |
| Last Modified: | 14 Jul 2025 05:01 |
| URI: | http://repository.its.ac.id/id/eprint/119628 |
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