PENENTUAN PORTOFOLIO DAN VALUE AT RISK MENGGUNAKAN MODEL ARMA-GARCH

Authors

  • Adellara Mutya R
  • Maiyastri Maiyastri
  • Yudiantri Asdi

DOI:

https://doi.org/10.25077/jmu.8.1.1-8.2019

Abstract

Dalam dunia investasi saham merupakan bentuk yang paling populer di kalangan masyarakat. Pada saham terdapat nilai risiko dan nilai ekspektasi return yang perlu dipertimbangkan oleh investor. Nilai Ekspektasi return dapat dihitung menggunakan model analisis deret waktu yaitu ARMA, sedangkan nilai risiko dapat diukur menggunakan beberapa metode salah satunya adalah metode Value at Risk (VaR). Untuk menghitung VaR diperlukan komponen volatilitas. Volatilitas dapat diestimasi menggunakan analisis deret waktu yaitu GARCH. Pada penelitian ini, peramalan dilakukan menggunakan data harga penutupan saham PT Astra Internasional Tbk, PT Bank Central Asia Tbk, PT Bank Negara Indonesia Tbk, PT Bank Rakyat Indonesia Tbk, dan PT Telekomunikasi Indonesia Tbk. Model terbaik yang didapatkan untuk mengestimasi nilai ekspektasi retrun diantaranya MA(1) untuk PT Astra Internasional Tbk, AR(1) untuk PT Bank Central Asia Tbk, ARMA(1,1) untuk PT Bank Rakyat Indonesia Tbk, MA(1) untuk PT Bank Rakyat Indonesia Tbk, dan MA(1) untuk PT Telekomunikasi Indonesia. Sedangkan model terbaik untuk mengestimasi nilai volatilitas adalah GARCH(1,1) untuk masing-masing perusahaan. Dengan menggunakan model ARMA-GARCH yang telah diestimasi diperoleh nilai VaR terbesar sampai terkecil secara berturut-turut terjadi pada saham PT Bank Negara Indonesia Tbk, PT Astra Internasional Tbk, PT Bank Rakyat Indonesia Tbk, PT Telekomunikasi Indonesia Tbk, dan PT Bank Central Asia Tbk. Bobot portofolio yang diperoleh adalah 5.47% untuk saham PT Astra Internasional Tbk, 44.52% untuk saham PT Bank Central Asia Tbk, 1.49% untuk saham PT Bank Negara Indonesia Tbk, 6.48% untuk saham PT Bank Rakyat Indonsia Tbk, dan 42.02% untuk saham PT Telekomunikasi Indonesia Tbk.

Kata Kunci: VaR, Portofolio, ARMA, GARCH

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Published

05-07-2019

Issue

Vol. 8 No. 1 (2019)

Section

Articles

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