Abstract:
This article investigates a stochastic filtering problem whereby the borrower’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.