2019
DOI: 10.21098/jimf.v5i4.1081
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A Markov Chain Model for Islamic Micro-Financing

Abstract: This paper introduces a Markov chain model for Islamic micro-financing, especially mudarabah  and murababah contract. Mudarabah and murabahah  are two Islamic micro-financing contracts that have enormous potential in creating a balance between the monetary and sharia sector because these two products are moving to manage the business sector which undoubtedly adds value to the economic movement directly.  On the other hand, these two contracts have the potential to cause problems in their implementation. The mo… Show more

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Cited by 2 publications
(3 citation statements)
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“…Relying on Monte Carlo simulation, they indicate that a high profit-sharing ratio is needed to avoid asymmetric information, meaning that such a ratio can avoid adverse selection. Lessy et al (2019) present a Markov chain model as a solution to adverse selection in PLS (mudarabah) contracts. According to them, the probability value of an applicant receiving investment from an Islamic bank will influence the possibility of adverse selection.…”
Section: Previous Studiesmentioning
confidence: 99%
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“…Relying on Monte Carlo simulation, they indicate that a high profit-sharing ratio is needed to avoid asymmetric information, meaning that such a ratio can avoid adverse selection. Lessy et al (2019) present a Markov chain model as a solution to adverse selection in PLS (mudarabah) contracts. According to them, the probability value of an applicant receiving investment from an Islamic bank will influence the possibility of adverse selection.…”
Section: Previous Studiesmentioning
confidence: 99%
“…A PLS contract involves high risk due to adverse selection and moral hazard problems. According to Lessy, Koudjeti, Diener, and Diener (2019), the risk associated with such a contract can occur at the time of the contract being agreed or during the contract period. In the related literature, several studies have considered the problems related to PLS contracts.…”
Section: Introduction 11 Backgroundmentioning
confidence: 99%
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