# Controlling systemic risk - network structures that minimize it and node properties to calculate it

## TL;DR

This paper introduces an approximate systemic risk evaluation method based on node properties, demonstrating its effectiveness and analyzing how network structures influence systemic risk in financial networks.

## Contribution

It proposes a new approximation method for systemic risk assessment using only node properties, reducing reliance on detailed inter-institution exposure data.

## Key findings

- Approximate method captures a large portion of systemic risk measured by Debt Rank.
- Network structures with high systemic risk are scalar assortative, with risky banks exposed to other risky banks.
- Disassortative networks with interactions between risky and stable banks are less risky.

## Abstract

Evaluation of systemic risk in networks of financial institutions in general requires information of inter-institution financial exposures. In the framework of Debt Rank algorithm, we introduce an approximate method of systemic risk evaluation which requires only node properties, such as total assets and liabilities, as inputs. We demonstrate that this approximation captures a large portion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlo simulations, we investigate network structures that can amplify systemic risk. Indeed, while no topology in general sense is {\em a priori} more stable if the market is liquid [1], a larger complexity is detrimental for the overall stability [2]. Here we find that the measure of scalar assortativity correlates well with level of systemic risk. In particular, network structures with high systemic risk are scalar assortative, meaning that risky banks are mostly exposed to other risky banks. Network structures with low systemic risk are scalar disassortative, with interactions of risky banks with stable banks.