Credit default swap (CDS) prediction model & trading strategy

This project focuses on the study of different explanatory models for the behavior of CDS security, such as Fixed-Effect Model, GLS Random-Effect Model, Pooled OLS and Quantile Regression Model. After determining the best fitness model, trading strategies with long and short positions in CDS have been developed. Due to some specifications of CDS, I conclude that the quantile regression is the most efficient model to estimate the data. The P&L and Sharpe Ratio of the strategy are analyzed using a backtesting analogy, where I conclude that, mainly for non-financial companies, the model allows traders to take advantage of and profit from arbitrages.

Cross sectional default probabilities in european corporate bonds

This study attempts to identify basis-trading opportunities in the European banking sector by comparing two different measures for the market’s assessment of risk: market-observed CDS spreads and model-implied Z-spreads. Using a sample of 10 banks, over a period of 3 years following the European banking crisis, it can be concluded that there were arbitrage opportunities in the sector, as evidenced by the derived negative bases.