Forecasting corporate credit spreads: regime-switching in LSTM

Abstract

A long short-term memory model (LSTM) which utilises regime-switching state information as a feature to predict the change of credit spreads is developed. Latent changes in the market are filtered out from observable credit spread time series. These hidden information of regime changes are incorporated into an LSTM, where the state probability is utilised as a feature for one-step ahead predictions of the credit spreads. Firstly, time series from corporate credit spreads are modelled through a Hidden Markov model (HMM) which is based on a discretised Ornstein-Uhlenbeck process. State-related information of the Markov chain, like the jump frequency and state occupation time hidden in the observed spreads are filtered out and adaptive HMM filters are built to estimate probabilities of hidden market states. The performance of the LSTM with regime-switching information is analysed and compared to the accuracy of a pure LSTM without state features. Furthermore, purely utilising the HMM forecast, the prediction of the credit spread is compared to the prediction within the LSTM. Beyond a simulations study, the HMM-LSTM model is calibrated on corporate credit spreads from three European countries between 2004 and 2019. The findings show that the LSTM forecast error is improved when regime information is added, mostly in cases with stronger market fluctuations.