Analyzing the profit-loss sharing contracts with Markov model
The purpose of this paper is to examine how to use first order Markov chain to build a reliable monitoring system for the profit-loss sharing based contracts (PLS) as the mode of financing contracts in Islamic bank with censored continuous-time observations. The paper adopts the longitudinal analysi...
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Komunitas Ilmuwan dan Profesional Muslim Indonesia
2016-12-01
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doaj-5b0b6bbdef36466ab90e50bdd17edf0d2020-11-24T23:19:50ZengKomunitas Ilmuwan dan Profesional Muslim IndonesiaCommunications in Science and Technology2502-92582502-92662016-12-0112788810.21924/cst.1.2.2016.17Analyzing the profit-loss sharing contracts with Markov modelImam Wahyudi0Ali Sakti1Department of Management, Faculty of Economics and Business, Universitas IndonesiaBank IndonesiaThe purpose of this paper is to examine how to use first order Markov chain to build a reliable monitoring system for the profit-loss sharing based contracts (PLS) as the mode of financing contracts in Islamic bank with censored continuous-time observations. The paper adopts the longitudinal analysis with the first order Markov chain framework. Laplace transform was used with homogenous continuous time assumption, from discretized generator matrix, to generate the transition matrix. Various metrics, i.e.: eigenvalue and eigenvector were used to test the first order Markov chain assumption. Cox semi parametric model was used also to analyze the momentum and waiting time effect as non-Markov behavior. The result shows that first order Markov chain is powerful as a monitoring tool for Islamic banks. We find that waiting time negatively affected present rating downgrade (upgrade) significantly. Likewise, momentum covariate showed negative effect. Finally, the result confirms that different origin rating have different movement behavior. The paper explores the potential of Markov chain framework as a risk management tool for Islamic banks. It provides valuable insight and integrative model for banks to manage their borrower accounts. This model can be developed to be a powerful early warning system to identify which borrower needs to be monitored intensively. Ultimately, this model could potentially increase the efficiency, productivity and competitiveness of Islamic banks in Indonesia. The analysis used only rating data. Further study should be able to give additional information about the determinant factors of rating movement of the borrowers by incorporating various factors such as contract-related factors, bank-related factors, borrower-related factors and macroeconomic factors.https://cst.kipmi.or.id/index.php/cst/article/view/17/13profit-loss sharingIslamic bankingdefault riskMarkov modelsurvival rate |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Imam Wahyudi Ali Sakti |
spellingShingle |
Imam Wahyudi Ali Sakti Analyzing the profit-loss sharing contracts with Markov model Communications in Science and Technology profit-loss sharing Islamic banking default risk Markov model survival rate |
author_facet |
Imam Wahyudi Ali Sakti |
author_sort |
Imam Wahyudi |
title |
Analyzing the profit-loss sharing contracts with Markov model |
title_short |
Analyzing the profit-loss sharing contracts with Markov model |
title_full |
Analyzing the profit-loss sharing contracts with Markov model |
title_fullStr |
Analyzing the profit-loss sharing contracts with Markov model |
title_full_unstemmed |
Analyzing the profit-loss sharing contracts with Markov model |
title_sort |
analyzing the profit-loss sharing contracts with markov model |
publisher |
Komunitas Ilmuwan dan Profesional Muslim Indonesia |
series |
Communications in Science and Technology |
issn |
2502-9258 2502-9266 |
publishDate |
2016-12-01 |
description |
The purpose of this paper is to examine how to use first order Markov chain to build a reliable monitoring system for the profit-loss sharing based contracts (PLS) as the mode of financing contracts in Islamic bank with censored continuous-time observations. The paper adopts the longitudinal analysis with the first order Markov chain framework. Laplace transform was used with homogenous continuous time assumption, from discretized generator matrix, to generate the transition matrix. Various metrics, i.e.: eigenvalue and eigenvector were used to test the first order Markov chain assumption. Cox semi parametric model was used also to analyze the momentum and waiting time effect as non-Markov behavior. The result shows that first order Markov chain is powerful as a monitoring tool for Islamic banks. We find that waiting time negatively affected present rating downgrade (upgrade) significantly. Likewise, momentum covariate showed negative effect. Finally, the result confirms that different origin rating have different movement behavior. The paper explores the potential of Markov chain framework as a risk management tool for Islamic banks. It provides valuable insight and integrative model for banks to manage their borrower accounts. This model can be developed to be a powerful early warning system to identify which borrower needs to be monitored intensively. Ultimately, this model could potentially increase the efficiency, productivity and competitiveness of Islamic banks in Indonesia. The analysis used only rating data. Further study should be able to give additional information about the determinant factors of rating movement of the borrowers by incorporating various factors such as contract-related factors, bank-related factors, borrower-related factors and macroeconomic factors. |
topic |
profit-loss sharing Islamic banking default risk Markov model survival rate |
url |
https://cst.kipmi.or.id/index.php/cst/article/view/17/13 |
work_keys_str_mv |
AT imamwahyudi analyzingtheprofitlosssharingcontractswithmarkovmodel AT alisakti analyzingtheprofitlosssharingcontractswithmarkovmodel |
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1725576579364421632 |