Measuring Counterparty Risk - Development of innovative Methods in Light of Regulatory Reforms

This cumulative doctoral thesis amends the literature on modeling counterparty credit risk exposures. The calculation of exposure measures for counterparty credit risk is a crucial task for financial institutions as it is required for various aspects, such as derivatives valuation, assessment of capital requirements and limitation. The Global Financial Crisis (GFC) has led to a major reform of the regulatory framework for counterparty risk and the OTC derivatives market. The new regulatory requirements lead to challenges in the modeling of counterparty credit risk exposures. The thesis consists of three independent research papers, which analyze and tackle three selected issues resulting from the introduction of the new supervisory standardized approach for measuring CCR exposures (SA-CCR) and the mandatory exchange of initial margin (IM) for a multitude of non-centrally cleared derivatives. The first research paper (chapter 1) reveals weaknesses and flaws in the methodology and calibration of the SA-CCR regarding the treatment of equity options by performing a profound methodological and empirical analysis. Based on these findings, measures for improving the SA-CCR are proposed. Within the second and third research paper, innovative approaches are developed which complement the toolbox for counterparty risk management. The second research paper (chapter 2) utilizes and modifies the SA-CCR’s model framework to develop a new semi-analytical method for the generation of exposure profiles for CVA calculation. The modified SA-CCR offers a fast and simple approach for the treatment of derivatives which are not covered by advanced exposure models. The third research paper (chapter 3) introduces an innovative method for forecasting future IM requirements under ISDA-SIMM™ based on forward sensitivities. This approach utilizes information and methodological building blocks already available in a Monte Carlo framework and adopts principles from industrial just-in-time manufacturing. Hence, the approach is capable of generating fast and accurate forward sensitivities