The purpose of this thesis is to investigate whether postings of collateral is an effective mitigation tool against counterparty risk for an interest rate swap between Bank of America and JPMorgan Chase in a general wrong-way risk scenario, namely the COVID-19 crisis. To do this, the thesis will seek to quantify the value of counterparty risk (known as CVA) through a two-part analysis. First, CVA is calculated on two almost identical interest rate swaps, the only difference being that one is collateralized, on their settlement date. This settlement date predates the beginning of the COVID-19 Pandemic. These CVA calculations are based on simulated interest rates and market data, as it would have been on the initial settlement date. Second, an empirical analysis back-tests these CVA calculations using actual data obtained from the COVID-19 Pandemic. The thesis will then compare the results and analyse the effectiveness of collateral. Throughout the thesis both the theory and importance of counterparty risk management is explained. Additionally, both the characteristics of interest rate swaps and the global derivatives market are described. This thesis will also seek to describe, model and calculate the components of counterparty risk: exposure, loss given default, probability of default, collateral calls and general wrong-way risk. The modelling and calculations will be done using the open source coding language, Python. The project concludes that collateral is a great tool for counterparty risk mitigation as it was able to mitigate between 49.9-53% of CVA. However, the empirical CVA remained at an elevated level even after collateralization. This was partially driven by a large spike in probability of default, which collateral was not able to mitigate. This led the thesis to conclude that collateral might be most effective when combined with other mitigation methods.
|Educations||MSc in International Business, (Graduate Programme) Final Thesis|
|Number of pages||100|