Estimasi Probabilitas Gagal Bayar Obligasi Korporasi Menggunakan Rantai Markov - Corporate Bond Default Probability Estimation Using Markov Chain

Rahmanda, Bintang (2018) Estimasi Probabilitas Gagal Bayar Obligasi Korporasi Menggunakan Rantai Markov - Corporate Bond Default Probability Estimation Using Markov Chain. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Obligasi memiliki resiko gagal bayar (resiko kredit) atas kewajiban utangnya pada saat jatuh tempo. Digunakan indeks peringkat kredit sebagai tolak ukur resiko gagal bayar yang pada umumnya berkisar antara indeks AAA sampai D, dimana indeks D merupakan indeks gagal bayar. Indeks peringkat obligasi dapat berubah seiring dengan berjalannya waktu, sehingga besarnya kemungkinan suatu obligasi untuk mengalami gagal bayar tidak diketahui. Dalam Tugas Akhir ini, digunakan Rantai Markov Diskrit untuk mengkaji transisi state berdasarkan data historis indeks peringkat obligasi korporasi yang bersumber dari PT. Pemeringkat Efek Indonesia tahun 2009-2014. Probabilitas transisi tiap state dihitung menggunakan Metode Cohort, serta uji validasinya dengan mengonstruksi Selang Kepercayaan Clopper-Pearson. Melalui simulasi Matlab, diperoleh matriks transisi probabilitas 9×9 sesuai dengan ciri Rantai Markov Diskrit; bersifat reducible dan menyerap; dengan state space urutan peringkat kredit dan kondisi dimana obligasi sudah ditarik dari peredaran. Hasil estimasi gagal bayar menunjukan bahwa peringkat A merupakan peringkat obligasi yang diyakini akan kemungkinan gagal bayarnya sebesar 0.0354.================================================================================================================= Credit risk causing bond to fail paying its debt obligation at the moment or after the maturity. One of the parameter to credit risk is credit rating index that usually goes around AAA to D, which D is the lowest grade index; also known as default index. Credit rating index would change throughout the time, so that the future event of default case to certain bonds is likely to be unknown. In this Final Project, Discrete Time Markov Chain were used to describe the transition among the defined states; based on the historical data of corporate bonds credit rating index gained from PT. Pemeringkat efek Indonesia from year 2009-2014. Each transition probability was calculated by using Cohort Method, as the validation of each default probability were done by constructing the Clopper-Pearson Confidence Interval. Through Matlab Simulations, the 9×9 transition probability matrix that represent the Discrete Time Markov Chain absorbing and reducible property were obtained; with the state space of credit rating indexes and an additional state of no longer rated bonds. The default probability estimation result showed that the bond with “A” rating is the firmest bond to be defaulted by 0.0354.

Item Type: Thesis (Undergraduate)
Additional Information: RSMa 519.233 Rah e
Uncontrolled Keywords: Default, Rantai Markov, probabilitas transisi, indeks peringkat kredit
Subjects: H Social Sciences > HA Statistics > HA31.7 Estimation
Q Science > QA Mathematics
Divisions: Faculty of Mathematics, Computation, and Data Science > Mathematics > (S1) Undergraduate Theses
Depositing User: Bintang Rahmanda
Date Deposited: 15 Jan 2019 07:15
Last Modified: 15 Jan 2019 07:15
URI: http://repository.its.ac.id/id/eprint/58560

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