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3847. Modelling shock propagation and resilience in financial temporal networks
Invited abstract in session TD-2: New Tools in Insurance Risk Management , stream OR in Banking, Finance and Insurance: New Tools for Risk Management.
Tuesday, 14:30-16:00Room: Glassalen (building: 101)
Authors (first author is the speaker)
| 1. | Giorgio Rizzini
|
| Department of Economics and Management, University of Brescia | |
| 2. | Fabrizio Lillo
|
| Scuola Normale Superiore and Università di Bologna |
Abstract
We provide a model for shock propagation and resilience in a temporal network. Each node is a bank and two banks are connected through an arc if a bank lends money at least once to another bank in a period. We assume that each node is associated with two latent parameters, i.e. the fitnesses, describing its propensity to create incoming and outgoing links at any time. At each time, the presence of an arc in the network is an independent realization of a Bernoulli random variable with probability specified by a link function of the fitness of two nodes. We assume that the fitness vector evolves accordingly to a VAR(1) through which it is possible to model the lagged interactions between fitness. In this framework, our aim is to study the resilience of a temporal network by proposing a modification of the impulse response analysis, which, in the network case, is a non-linear function of the shock intensity. We study on how a shock on a node of a temporal network propagates and how the system relaxes back to the equilibrium state. We also propose a novel econometric estimation model that combines the Maximum Likelihood Estimation method and the Kalman filter to estimate the fitness'dynamics. We test our methodology to synthetic networks and then we apply it to the electronic Market of Interbank Deposit network. The analysis is suitable to detect evolutionary features and the relationships among financial institutions highlighting which are the critical banks in shock spreading
Keywords
- Financial Modelling
- Stochastic Models
- Analytics and Data Science
Status: accepted
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