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Snijders, T., Steglich, C. and Schweinberger, M. (2017) Modeling the Coevolution of Networks and Behavior. In: van Montfort, K., Oud, J. and Satorra, A., Eds., Longitudinal Models in the Behavioral and Related Sciences, Routledge, London, 41-71.
https://doi.org/10.4324/9781315091655-3

has been cited by the following article:

• TITLE: Credit Scoring with Ego-Network Data

  AUTHORS: Stanley Sewe, Philip Ngare, Patrick Weke

  KEYWORDS: Stochastic Filtering, Bayesian Updating, Credit Scoring, Filtration, Ego-Network

  JOURNAL NAME: Journal of Mathematical Finance, Vol.9 No.3, August 22, 2019

  ABSTRACT: This article investigates a stochastic filtering problem whereby the bor-rower’s hidden credit quality is estimated using ego-network signals. The hidden credit quality process is modeled as a mean reverting Ornstein-Ulehnbeck process. The lender observes the borrower’s behavior modeled as a continuous time diffusion process. The drift of the diffusion process is driven by the hidden credit quality. At discrete fixed times, the lender gets ego-network signals from the borrower and the borrower’s direct friends. The observation filtration thus contains continuous time borrower data augmented with discrete time ego-network signals. Combining the continuous time observation data and ego-network information, we derive filter equations for the hidden process and the properties of the conditional variance. Further, we study the asymptotic properties of the conditional variance when the frequency of arrival of ego-network signals is increased.