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PENGUKURAN NILAI RISIKO PORTOFOLIO SAHAM PADA INDEKS LQ45 DI BIDANG TELEKOMUNIKASI MENGUNAKAN METODE KOPULA CLAYTON

*Salsabila Syifa Binsanno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Di Asih I Maruddani  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The characteristic of copula is non strict on certain distribution assumptions, can explain nonlinier relationship, and easily construct distribution through the marginals that do not need to come from the same distribution family. Copula will be useful for stock data that has price charts fluctuate rapidly and risk will always follow in investing. The relation between risk and copula in this study is to calculate the risk value in the stock portfolio using VaR with the generation of Monte Carlo simulation through Clayton copula on four companies engaged in telecommunications sector, namely EXCL.JK (PT XL Axiata Tbk), TLKM.JK (PT Telekomunikasi Indonesia Tbk), TOWR.JK (PT Sarana Menara Nusantara Tbk), and TBIG.JK (PT Tower Bersama Infrastructure Tbk) for period 2 January 2020 to 31 December 2021. This study resulted that the selected stock portfolios are EXCL and TBIG which had the highest risk value of -0,062741 at 99% confidence level, so when an investor will invest Rp100.000.000,00 the maximum estimated risk is Rp.6.274.100 within one day.

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Pengukuran Nilai Risiko Portofolio Saham pada Indeks LQ45 di Bidang Telekomunikasi Menggunakan Kopula Clayton
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Keywords: Portofolio; Clayton Copula; ARIMA-ARCH/GARCH; Value at Risk.

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  1. Adnyana, I. M. (2020). Manajemen Investasi dan Portofolio. Jakarta Selatan: LPU-UNAS
  2. Best, P. (1998). Implementing Value at Risk. West Sussex: John Wiley & Sons
  3. Budiani, J. R., Sutikno, & Purhadi. (2015). Analisis Hubungan dan pemodelan Luas Panen Padi dengan Indikator El-Nino Suuthern Oscilaation (ENSO) di Kabupaten Bojonegoro Melalui Pendekatan Copula dan Regresi Robust M-Estimation. Jurnal Sains dan Seni ITS Vol.4 No.2, 2337-3520
  4. Embrechts, P., Lindskog, F., & McNeil, A. (2001). Modelling Dependence with Copulas and Applications to Risk Management. Switzerland: Departmet of Mathematics ETHZ
  5. Fitriawati, A., Febrianti, W., Bustan, A. W. & A., 2020. Teknik Mengkonstruksi Distribusi Bivariat Copula Clayton pada Data Marginal Diskrit dengan Implikasi Kebergantungan. Jurnal Ilmiah Pendidikan Matematika, 8(2), pp. 227-238
  6. Hanafi, M. M. (2016). Manajemen Risiko Edisi Ketiga. Yogyakarta: UPP STIM YKPN
  7. Handini, J. A., Maruddani, D. I., & Safitri, D. (2018). Copula Frank pada Value at Risk (VaR) Pembentukan Portofolio Bivariat. Jurnal Gaussian, 293-302
  8. Hutomo, M. P., Dewi, A. S. & Gustyana, T. T., 2017. Analisis VaR pada Saham Perusahaan Properti yang Terdaftar pada Indeks LQ45. e-Proceeding of Management : Vo.4, No.3, pp. 2316-2323
  9. Jorion, P. (2007). Value at Risk The New Benchmark for Managing Financial Risk Third Edition. New York: McGraw-Hill
  10. Juanda, B., & Junaidi. (2011). Ekonometrika Deret Waktu Teori dan Aplikasi. Bogor: IPB Press
  11. Kwak, Y. H., & Ingall, L. (2009). Exploring Monte Carlo Simulation Applications for project Management. IEEE ENGINEERING MANAGEMENT REVIEW. Vol.37, No.2, Second Quarter, 83-91
  12. Lembang, F. K. (2014). Evaluasi Dampak Krisis Moneter, Bom Bali I dan II terhadap Jumlah Kunjungan Wisatawan ke Bali dengan Regresi Time Series, Regresi Dummy dan Intervensi. Seminar Nasional Basic Science VI F-MIPA UNPATTI, 137-150
  13. Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1997). Forecasting Methods and Application Third Edition. New York: Wiley
  14. Maruddani, D. I., & Purbowati, A. (2009). Pengukuran Value at Risk pada Aset Tunggal dan Portofoilo dengan Simulasi Monte Carlo. Media Statistika, 93-104
  15. Nelsen, R. B. (2006). An Introduction to Copula. New York: Springer
  16. Nurcahyani, A. W., Saputro, D. S., & Kurdhi, N. A. (2016). Korelasi Kendall untuk Estimasi Parameter Distribusi Clayton-copula Bivariat. Seminar Nasional Matematika dan Pendidikan Matematika UNY, MS 53-MS 58
  17. Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models Concepts and Cases. Canada: John Wiley & Sons. Inc
  18. Prihatiningsih, D. R., Maruddani, D. I., & Rahmawati, R. (2020). Value at Risk (VaR) dan Conditional Value at Risk (CVaR) Dalam Pembentukan Portofolio Bivariat Menggunakan Copula Gumbel. Jurnal Gaussian, 326-335
  19. Rinadi, G. A., Sasongko, L. R., & Susanto, B. (2019). Regresi Median pada Copula Bivariat. Jurnal Teori dan Aplikasi Matematika, 07-14
  20. Suyono. (2015). Analisis Regresi Untuk Penelitian. Yogyakarta: Deepublish
  21. Tandelilin, E. (2010). Portofolio dan Investasi : Teori dan Aplikasi. Yoyakarta: Kanisius
  22. Von, V., & Hain, J. (2010). Comparison of common tests for normality. Germany: Julius Maximilians Universitat Wurzburg
  23. Wei, W. (2006). Time Series Analysis Univariate and Multivariate Methods Second Edition. New York: Pearson Education, Inc

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