Credit default swaps prediction by using an FTS-ANN model

Credit Default Swap (CDS) is a derivative instrument that serves as insurance against the credit risk of countries or firms. Especially, since the 2008 global crisis, it has received much attention as a risk indicator in financial markets. Given the role played by CDS prices in determining the creditworthiness of banks, corporations or countries, even in predicting financial crisis, it is clear that there has been a need for models that can produce results close to real values due to the nonlinear and chaotic nature of CDS prices in fragile economies. In this study, Türkiye is analyzed as a fragile economy with a high CDS premium. To do this, the artificial neural network (ANN) is combined with the fuzzy time series (FTS) in order to construct a novel model called FTS-ANN. Based on this novel model, the predicted results are evaluated using different well-known statistical techniques. It is found that the epoch and regression $R$ values of the proposed model are 8 and 0.99554. This shows that our model outperforms other models. Finally, the expected contribution of our model is that this model, which gives very good results for a fragile economy like Türkiye, can be adapted to the CDS values of other countries.