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Abstract
We study the problem of finding the worst-case joint distribution of a set of risk factors given prescribed multivariate marginals and a non-linear loss function. We show that when the risk measure is conditional value-at-risk (CVaR), and the distributions are discretised, the problem can be conveniently solved using linear programming. The method has applications to any situation where marginals are provided and bounds need to be determined on total portfolio risk. In this paper, we emphasise applications to counterparty credit risk including the assessment of wrong-way risk. A suitable algorithm for counterparty risk measurement of a real portfolio is also presented.
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Author's Biography
Amir Memartoluie is a PhD candidate at Cheriton School of Computer Science at University of Waterloo. He received his Master in Quantitative Finance from the Center for Advanced Studies in Finance at University of Waterloo in 2009. Prior to that, he studied applied mathematics and statistics and received his MSc in statistics from Concordia University in 2007 and BSc in applied mathematics from Department of Mathematics at KNT University of Technology. In addition to that he has represented Faculty of Mathematics at Graduate Studies Endowment Fund at University of Waterloo from 2011 to 2016.
David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He is the author of many articles on the subjects of risk management, portfolio optimisation and derivatives pricing, and regularly serves as a consultant for financial institutions and software companies.
Tony Wirjanto is an econometrician by training with research focusing on financial econometrics and financial time series. In particular, his research interests lie in developing statistical methodology for applications in finance and finance-related areas.
Citation
Memartoluie, Amir, Saunders, David and Wirjanto, Tony (2017, April 1). Wrong-way risk bounds in counterparty credit risk management. In the Journal of Risk Management in Financial Institutions, Volume 10, Issue 2. https://doi.org/10.69554/ZNGY3844.Publications LLP