Statistical Methods in Finance 2023

                                

Abstract

Structure recovery and trend estimation for dynamic network analysis

By Sayantan Banerjee
Indian Institute of Management, Indore

Abstract Dynamic network analysis has attracted a great deal of attention in recent years. We propose a block-structured dynamic exponential-family random graph model (ERGM), where the time domain is divided into consecutive blocks and the network parameters are assumed to evolve smoothly within each block. In particular, we let the latent ERGM parameters follow a piecewise polynomial model with an unknown block structure (e.g., change points). We propose an iterative estimation procedure that involves estimating the block structure using trend filtering and fitting ERGMs for networks belonging to the same time block. We demonstrate the utility of the proposed approach using simulation studies and applications to interbank transaction data in finance.

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