Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs

Using macroeconomic and financial conditions to forecast credit default swap (CDS) spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified LSTM model formed by integrating with the Merton determinants model, to forecast the CDS indices. We provide the rigorous math behind the Merton-LSTM model, which demonstrates that by leveraging the nonlinear learning ability of LSTM with increased model capacity, the Merton-LSTM model is expected to learn the inherent association between the Merton determinants and CDS spreads. Further, the Merton-LSTM model is compared with the machine learning models LSTM, gated recurrent unit (GRU), multilayer perceptron network (MLP), support vector machine (SVM) and a typical sto-chastic series model in forecasting the two most liquid five-year CDS indices, North America High Yield index (CDX.NA.HY) and North America Investment Grade index (CDX.NA.IG) through the root mean squared error (RMSE) and the Diebold-Mariano test. The comparison results show that the RMSEs of the Merton-LSTM model are the lowest (6.2570-27.2000 for CDX.NA.HY and 1.3168-6.4772 for CDX.NA.IG) compared to other competitive models. The superiority of the Merton-LSTM model in forecasting performance is highlighted in long-term prediction even with a forecasting horizon extended to 28 days. Simulated trading with different thresholds and horizons is conducted in this study. We find that the Merton-LSTM trading strategy yields the highest annualized Sharpe ratios and lowest maximum losses at most thresholds and horizons, highlighting the economic significance of the proposed model.