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2019 Dynamic Credit Quality Evaluation with Social Network Data
Stanley Sewe, Philip Ngare, Patrick Weke
J. Appl. Math. 2019: 1-11 (2019). DOI: 10.1155/2019/8350464


We investigate the filtering problem where the borrower’s time varying credit quality process is estimated using continuous time observation process and her (in this paper we refer to the borrower as female and the lender as male) ego-network data. The hidden credit quality is modeled as a hidden Gaussian mean-reverting process whilst the social network is modeled as a continuous time latent space network model. At discrete times, the network data provides unbiased estimates of the current credit state of the borrower and her ego-network. Combining the continuous time observed behavioral data and network information, we provide filter equations for the hidden credit quality and show how the network information reduces information asymmetry between the borrower and the lender. Further, we consider the case when the network information arrival times are random and solve stochastic optimal control problem for a lender having linear quadratic utility function.


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Stanley Sewe. Philip Ngare. Patrick Weke. "Dynamic Credit Quality Evaluation with Social Network Data." J. Appl. Math. 2019 1 - 11, 2019.


Received: 2 November 2018; Accepted: 3 February 2019; Published: 2019
First available in Project Euclid: 16 May 2019

zbMATH: 07132125
MathSciNet: MR3939000
Digital Object Identifier: 10.1155/2019/8350464

Rights: Copyright © 2019 Hindawi


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