Modeling Credit Default Swap Spreads with Transformers : A Thesis in collaboration with Handelsbanken

Sammanfattning: In the aftermath of the credit crisis in 2007, the importance of Credit Valuation Adjustment (CVA) rose in the Over The Counter (OTC) derivative pricing process. One important part of the pricing process is to determine Probability of Defaults (PDs) of the counterparty in question. The normal way of doing this is to use Credit Default Swap (CDS) spreads from the CDS market. In some cases, there is no associated liquid CDS market, and in those cases, it is market practice to use proxy CDS spreads. In this thesis, transformer models are used to generate proxy CDS spreads with a certain region, rating, and tenor from stand-alone CDS spread data. Two different models are created to do this. The first simpler model is an encoder-based model that uses stand-alone CDS data from a single company to generate one proxy spread per inference. The second, more advanced model is an encoder-decoder model that uses stand-alone CDS data from three companies to generate one proxy spread per inference. The performance of the models is compared, and it is shown that the more advanced model outperforms the simpler model. It should, be noted that the simpler model is faster to train. Both models could be used for data validation. To create the transformer models, it was necessary to implement custom embeddings that embedd specific corporate information and temporal information regarding the CDS spreads. The importance of the different embeddings was also investigated, and it is clear that certain embeddings are more important than others.