Free Webinar: Markus Hofer: Quantifying Systemic Risk using Bayesian Statistics
Webinar
Thursday, June 11, 2020 - 06:30 PM
Until: Thursday, June 11, 2020 - 08:30 PM
(Adjusted for timezone: Europe/London)
Categories
PLEASE NOTE THAT THIS WEBINAR WILL START ON THURSDAY, 11 JUNE, 2020, AT 6:30 PM ***LONDON TIME*** (01.30 PM ***EDT***)
TITLE: Quantifying Systemic Risk using Bayesian Networks
ABSTRACT:
We present a 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 handle complicated dependence structures e.g. in the context of wrong-way risk or risks which emerge from central clearing memberships. Our results show that stress propagation in an interconnected financial system can have a significant impact on counterparty credit losses.
BIOGRAPHY
Markus is a Quantitative Analyst in Financial Markets at Bayerische Landesbank (BayernLB) in Munich. Prior to moving to Munich he worked several years as a front office quant at ING Bank in Amsterdam. He has experience on a wide selection of financial products, including interest rate, commodity, credit, inflation and foreign exchange derivatives, and has published a number of scientific articles on XVA Modelling and Numerical Methods for Derivative Pricing. He holds a PhD in Applied Mathematics from Graz University of Technology on the subject of Monte Carlo Simulation in Derivative Pricing and Dependence Modelling.
LINKS TO PAPERS
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3525739
https://www.risk.net/cutting-edge/banking/7462701/quantifying-systemic-risk-using-bayesian-networks
For more information, please visit https://www.meetup.com/thalesians/events/270634344/
TITLE: Quantifying Systemic Risk using Bayesian Networks
ABSTRACT:
We present a 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 handle complicated dependence structures e.g. in the context of wrong-way risk or risks which emerge from central clearing memberships. Our results show that stress propagation in an interconnected financial system can have a significant impact on counterparty credit losses.
BIOGRAPHY
Markus is a Quantitative Analyst in Financial Markets at Bayerische Landesbank (BayernLB) in Munich. Prior to moving to Munich he worked several years as a front office quant at ING Bank in Amsterdam. He has experience on a wide selection of financial products, including interest rate, commodity, credit, inflation and foreign exchange derivatives, and has published a number of scientific articles on XVA Modelling and Numerical Methods for Derivative Pricing. He holds a PhD in Applied Mathematics from Graz University of Technology on the subject of Monte Carlo Simulation in Derivative Pricing and Dependence Modelling.
LINKS TO PAPERS
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3525739
https://www.risk.net/cutting-edge/banking/7462701/quantifying-systemic-risk-using-bayesian-networks
For more information, please visit https://www.meetup.com/thalesians/events/270634344/