Advanced modelling and efficient numerical algorithms for the estimation of xVA
Credit Value Adjustment is the difference between a bank’s portfolio value without counterparty credit risk and the value which takes into account the possible default of a counterparty. Funding and Capital value adjustments account for the funding and capital needs associated to trading derivatives. These family of adjustments (xVA) depend on many risk factors and are typically complex to measure for realistic portfolios. We propose an advanced mathematical modeling and numerical evaluation of xVA for financial derivatives portfolios. This project is funded by Technologiestichting STW, and we closely collaborate with ING Bank and Centrum voor Wiskunde en Informatica (CWI).
Researcher working on the project: Cees de Graaf
Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte Carlo Method. Journal of Computational Finance 21(1), pp. 83-113.
Efficient Computation of Exposure Profiles for Counterparty Credit Risk.International Journal of Theoretical and Applied Finance 17(4), 1450024..
Future-proof financial risk management: volatility and collateral modelling for MVA
Highly dynamic financial markets and regulatory reforms call for a continuous innovation in risk management, relying on advanced mathematical modelling and numerical methodologies. This project will integrate scientific research with the complexity of realistic market practices. The objective is to develop a multi-currency simulation-framework for interest rates, foreign exchange rates and collateral, incorporating the effects of a skewed volatility surface. The application of such a framework is twofold: we aim to quantify the impact of the volatility skew on financial risk metrics (MVA in particular, the latest member of the xVA family) and investigate the potential optimization of collateral posting. The project is carried out in close collaboration with ING and funded by the NWO Industrial Doctorate program, which is aimed at facilitating collaboration between knowledge institutions and industry.
Researcher working on the project: Jori Hoencamp
Data-driven methods for risk management in trading activities
This project is part of BigDataFinance, a H2020 Marie Sklodowska-Curie Innovative Training Network, in close collaboration with ING Bank Quantitative Analytics. The main objective is to draw upon data-driven methods and extract the interconnectedness between financial assets and firms, detect abrupt changes of regime, as well as study the evolution of relationships across different market states. Using this understanding, we will develop quantitative measures that can be incorporated in a financial institution’s risk analytics and modelling framework in the current dynamic regulatory environment.
Researcher working on the project: Ioannis Anagnostou
Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory. Complexity 2018, 6076173..
. Contagious defaults in a credit portfolio: a Bayesian network approach. Submitted.
Systemic risk: identification, valuation and risk management
Derivatives markets play a crucial role in the interconnectedness of financial systems, and currently a large volume (with outstanding notional amounts in trillions of USD) of derivatives are actively being traded between financial institutions, corporates and individual investors. Post the financial crisis of 2007-08, regulators have introduced a number of valuation adjustments (xVA) to cover credit, funding and capital cost which financial institutions face in OTC derivatives transactions. In context of xVA, wrong-way risk (WWR), which is caused by an adverse correlation between exposure and credit, can have significant consequences for risk management. Our objective is to use network-based methodologies for xVA modelling, focusing particularly on WWR.
Researcher working on the project: Sumit Sourabh
Liquidity risk in derivatives valuation: an improved credit proxy method. Quantitative Finance18(3), pp. 467-481..
Computational methods for the audit
The main objective is to develop a framework that uses and combines various advanced analytical methods for the purpose of the audit. This framework can be used to understand the structures of the entity and confirm our understanding of the entity being audited. Due to the increased operational complexity of large entities we propose to use advanced methods from the complex systems to build a mathematical representation of the entity. The most important contribution is to develop novel techniques using state-of-the-art computational methods to analyze available data of the entity to obtain audit evidence. We focus on creating procedures that can coexist with the existing audit methodologies.
Researcher working on the project: Marcel Boersma
M.Boersma, S. Sourabh, L. Hoogduin, and 4(4), pp. 59-85.. Financial statement networks: an application of network theory in audit. Journal of Network Theory in Finance