skip to main content

PENDEKATAN MODEL KMV MERTON UNTUK PENGUKURAN NILAI RISIKO KREDIT OBLIGASI EXPECTED DEFAULT FREQUENCY (EDF) DILENGKAPI GUI R

*Agil Setyo Anggoro  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Mustafid Mustafid  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Puspita Kartikasari  -  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.

Citation Format:
Abstract

Bonds are debt securities from the issuer to bondholders with a promise to pay off the principal and the coupon at maturity. Bond investing can generate income while also posing investment risks. One of the risks connected with bond investing is credit risk, which might manifest as a firm collapsing (default). The KMV Merton model approach is one method of measuring bond credit risk. This Merton KMV model computes the Expected Default Frequency (EDF), which is the likelihood of a firm failing in the following years or years. The data processing system using the Graphical User Interface (GUI) can facilitate the analysis process by implementing the Shiny Package in the R studio program. This research case makes use of up to 48 months of monthly corporate asset data from January 2018 to December 2021. The results obtained the value of Expected Default Frequency (EDF) in each company, namely PT Bank Mandiri Tbk obtained a value of 0% and PT Bank Rakyat Indonesia Tbk obtained a value of 1,406668E-113%. Because PT Bank Rakyat Indonesia Tbk's percentage return is higher than that of PT Bank Mandiri Tbk, investors would be better off investing in bonds at PT Bank Mandiri Tbk.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA Form
Subject
Type Research Instrument
  Download (453KB)    Indexing metadata
Keywords: Investment; Bonds; KMV Merton; Credit Risk; Expected Default Frequency (EDF); Shiny Package.

Article Metrics:

  1. Agus, S., Irwanto, A.K, dan Maulana, T.N.A. 2014. Analisis Pengaruh Rasio Keuangan terhadap Probabilitas Kebangkrutan Empat Bank dalam Kelompok Lq 45 di Bursa Efek Indonesia. Jurnal Widyariset Vol. 17, No.1 Hal: 163-174. DOI: http://dx.doi.org/10.14203/widyariset.17.1.2014.163-174
  2. Asdriargo, A., Maruddani, D.A.I., dan Hoyyi, A. 2012. Pengukuran Risiko Kredit Harga Obligasi Dengan Pendekatan Model Struktural KMV Merton. Jurnal Gaussian Vol. 1, No. 1, Hal: 11-20. DOI: https://doi.org/10.14710/j.gauss.v1i1.519
  3. Black, F., dan Scholes, M. 1973. The Pricing of Options and Corporate Liabilities. Journal of Political Economy Vol. 81, Hal: 637-654. DOI: http://dx.doi.org/10.1086/260062
  4. Dmouj, A. 2006. Stock Price Modeling: Theory and Practice. Amsterdam: BMI Paper
  5. Hadad, M.D., Santoso, W., dan Besar, D.S. 2004. Probabilitas Kegagalan Korporasi Dengan Menggunakan Model Merton. Jakarta: Research Paper Bank Indonesia
  6. Kasidi. 2014. Manajemen Risiko. Bogor: Ghalia Indonesia
  7. KSEI. 2022. Efek yang Terdaftar pada jenis Obligasi Korporasi. Tersedia: https://www.ksei.co.id/services/registered-securities/corporate-bonds (diakses pada tanggal 25 Oktober 2021)
  8. Kulkarni, A., Mishra, A.K., dan Thakker, J. 2006. How Good is Merton Model at Assessing Credit Risk? Evidence from India. India: National Institute of Bank Management
  9. Maruddani, D.A.I. 2011. Pengukuran Risiko Kredit Obligasi Dengan Model Merton. Jurnal Ekonomi, Manajemen, & Akuntansi Vol. 1, No. 1, Hal: 123-141
  10. Merton, R. 1974. On the Pricing of Corporate Debt: The Risk Structure of Interest Rate. Journal of Finance Vol. 29, No. 2, Hal: 449–470
  11. Najmuddin, dan Faisal, A. 2016. Komparasi Obligasi dan Sukuk Sebuah Tinjauan Fenomenologis. Jurnal Ekonomi dan Bisnis Islam Vol. 1, No. 1, Hal: 123-149
  12. Sugiyono .2013. Metode Penelitian Pendidikan (Pendekatan Kuantitatif, Kualitatif Dan R&D). Bandung: alfabeta
  13. Susanto, T.S., Sutejo, S. B., dan Marviano, D. 2016. Pengaruh Kinerja Keuangan Bank Terhadap Rating Obligasi Bank di Indonesia. Jurnal Manajemen Teori dan Terapan Vol. 5, No. 3, Hal: 167 -169
  14. Tandelilin, E. 2017. Pasar Modal Manajemen Portofolio & Investasi. Yogyakarta: PT Kanisius
  15. Tirta, I.M. 2014. Pengembangan E-Modul Statistika Terintegrasi dan Dinamik dengan R-shiny dan mathJax. Jember: Prosiding Seminar Nasional Matematika, Universitas Jember
  16. Von, V., dan Hain, J. 2010. Comparison of Common Tests for Normality. Skripsi. Program studi Matematika VIII (Statistik) Fakultas Matematika dan Ilmu Komputer Universitas Julius Maximilian of Wurzburg
  17. Walpole, R.E, Myers, R.H, Myres, S.L., dan Ye, K. 2016. Probability & Statistics for Engineers & Scientists. Boston: Pearson
  18. Yusof, N.M., dan Jaffar, M.M. 2015. Forecasting the Probability of Default of PN17 Company using KMV-Merton Model. International Journal of Applied Mathematics and Statistics Vol. 53, No. 5, Hal: 104-108

Last update:

No citation recorded.

Last update:

No citation recorded.