Title: Forecasting corporate credit spreads: Regime-switching in LSTM
Authors: Christina Erlwein-Sayer - University of Applied Sciences HTW Berlin (Germany) [presenting]
Stefanie Grimm - Fraunhofer Institute of Industrial Mathematics ITWM (Germany)
Alexander Pieper - University of Applied Sciences HTW Berlin (Germany)
Rumeysa Alsac - Fraunhofer Institute of Industrial Mathematics ITWM (Germany)
Abstract: Corporate credit spreads are modelled through a Hidden Markov model (HMM) which is based on a discretised Ornstein-Uhlenbeck model. We forecast the credit spreads within this HMM and filter out state-related information hidden in the observed spreads. We build a long short-term memory recurrent neural network (LSTM) which utilises the regime-switching information as a feature to predict the change of the credit spread. The performance of the LSTM is analysed and compared to the accuracy of an LSTM without regime-switching information. Furthermore, purely utilising the HMM forecast, the prediction of the credit spread is compared to the prediction within the LSTM. The HMM-LSTM model is calibrated on corporate credit spreads from three European countries between 2004 and 2019. Our findings show that in most cases the LSTM performance is improved when regime information is added.