Quantifying Systemic Risk Using Bayesian Networks

Autor:	Sumit Sourabh, Markus Hofer, Drona Kandhai
Rok vydání:	2020
Předmět:	Distress Risk model Computer science Econometrics Systemic risk Bayesian network Context (language use) Counterparty Scenario testing Credit risk
Zdroj:	SSRN Electronic Journal.
ISSN:	1556-5068
DOI:	10.2139/ssrn.3525739
Popis:	We develop a novel framework using Bayesian networks to capture distress dependence in the context of counterparty credit risk. This allows us to calibrate the probability of distress of an entity conditional on the distress of a different entity. We apply our methodology to wrong-way risk model proposed by Turlakov and stress scenario testing. Our results show that stress propagation in an interconnected financial system can have a significant impact on counterparty credit exposures.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b43964cc3064878e0ab2e664b4166767 https://doi.org/10.2139/ssrn.3525739 Zobrazit plný text záznamu