# Better estimation of financial risks possible with maths

Due to the recent financial crisis, the requirements imposed on banks have been made stricter. Banks must model the credit risk of the counterparties now in their portfolios, for instance. A measure for this is the credit value adjustment (CVA): the difference between the value of a portfolio without credit risk and the value if a possible bankruptcy of the counterparty is included. Qian Feng modelled CVAs and designed a new algorithm that can help banks estimate the risks precisely, so they can take appropriate measures if necessary.

Publication date: 13-04-2017

Due to the recent financial crisis, the requirements imposed on banks have been made stricter. Banks must model the credit risk of the counterparties now in their portfolios, for instance. A measure for this is the credit value adjustment (CVA): the difference between the value of a portfolio without credit risk and the value if a possible bankruptcy of the counterparty is included. Qian Feng, PhD student from Centrum Wiskunde & Informatica (CWI) in Amsterdam, modelled CVAs and designed a new algorithm that can help banks estimate the risks precisely, so they can take appropriate measures if necessary. She defended her PhD thesis ‘Advanced Estimation of Credit Valuation Adjustment’ at Delft University of Technology on 4 April. The resulting algorithm can compute the risk of high losses more precisely.

In an interview for Delft University of Technology Qian Feng said: “Since the recent financial crisis, financial modelling and risk management have changed. The Basel Committee on Banking Supervision has introduced new documentation with banking regulations. Banks should reserve a certain amount of capital to buffer for the default risk of all counterparties in their portfolios. The use of models that take this so-called counterparty credit risk into account, results in an increased computational demand within banks. This is a very critical problem. Within my research I have focused on the computational problems in pricing and measurements aspects of the counterparty credit risk. I have developed an efficient algorithm, based on the Stochastic Grid Bundling Method, for credit valuation adjustment. I hope that the industry will be interested in the algorithm. My dream is that banks will apply the algorithm widely.”

The project, which this PhD research was part of, was funded by NWO/STW. Participants of this broader project were Centrum Wiskunde & Informatica (CWI) and University of Amsterdam (UvA). The ING bank provided financial support to the project, and helped with the training of two PhD students. Besides ING Bank, VORtech Computing and EY were members of the project’s user committee. This research project demonstrates a successful collaboration between the academia and the industry.