Financial Systems

Computing Systemic Risk Measures with Graph Neural Networks ArXiv ID: 2410.07222 “View on arXiv” Authors: Unknown Abstract This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from Biagini, Fouque, Fritelli and Meyer-Brandis (2019) to graph structured data. In particular, we focus on an aggregation function that is derived from a market clearing algorithm proposed by Eisenberg and Noe (2001). In this setting, we show the existence of an optimal random allocation that distributes the overall minimal bailout capital and secures the network. We study numerical methods for the approximation of systemic risk and optimal random allocations. We propose to use permutation equivariant architectures of neural networks like graph neural networks (GNNs) and a class that we name (extended) permutation equivariant neural networks ((X)PENNs). We compare their performance to several benchmark allocations. The main feature of GNNs and (X)PENNs is that they are permutation equivariant with respect to the underlying graph data. In numerical experiments we find evidence that these permutation equivariant methods are superior to other approaches. ...

DEPLOYERS: An agent based modeling tool for multi country real world data ArXiv ID: 2409.04876 “View on arXiv” Authors: Unknown Abstract We present recent progress in the design and development of DEPLOYERS, an agent-based macroeconomics modeling (ABM) framework, capable to deploy and simulate a full economic system (individual workers, goods and services firms, government, central and private banks, financial market, external sectors) whose structure and activity analysis reproduce the desired calibration data, that can be, for example a Social Accounting Matrix (SAM) or a Supply-Use Table (SUT) or an Input-Output Table (IOT).Here we extend our previous work to a multi-country version and show an example using data from a 46-countries 64-sectors FIGARO Inter-Country IOT. The simulation of each country runs on a separate thread or CPU core to simulate the activity of one step (month, week, or day) and then interacts (updates imports, exports, transfer) with that country’s foreign partners, and proceeds to the next step. This interaction can be chosen to be aggregated (a single row and column IO account) or disaggregated (64 rows and columns) with each partner. A typical run simulates thousands of individuals and firms engaged in their monthly activity and then records the results, much like a survey of the country’s economic system. This data can then be subjected to, for example, an Input-Output analysis to find out the sources of observed stylized effects as a function of time in the detailed and realistic modeling environment that can be easily implemented in an ABM framework. ...

Principios de Finanzas (Principles ofFinance) ArXiv ID: ssrn-2313282 “View on arXiv” Authors: Unknown Abstract Spanish Abstract En esta monografía se describen doce principios que rigen las finanzas: el comportamiento financiero egoísta, las dos caras de la transa Keywords: Financial Principles, Selfish Financial Behavior, Financial Systems, Market Rules, Financial Monography, General Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The text describes foundational principles of finance in a descriptive, conceptual manner without mathematical derivations, backtests, or data-driven implementation. flowchart TD A["Research Goal<br>Identify 12 Principles Governing Finance"] --> B["Methodology<br>Descriptive Literature Review"] B --> C["Data Input<br>Financial Monography & Market Rules"] C --> D["Computational Process<br>Analysis of Selfish Financial Behavior"] D --> E["Key Findings<br>The 12 Core Principles of Finance"]