Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression

Expert Systems with Applications. 2022. Vol. 194. No. May 2022. Article 116553.

Vukovic D., Romanyuk K., Ivashchenko S., Grigorieva E.

This paper investigates the forecasting performance for credit default swap (CDS) spreads by Support Vector
Machines (SVM), Group Method of Data Handling (GMDH), Long Short-Term Memory (LSTM) and Markov
switching autoregression (MSA) for daily CDS spreads of the 513 leading US companies, in the period
2009–2020. The goal of this study is to test the forecasting performance of these methods before and during the
Covid-19 pandemic and to check whether there are changes in the market efficiency. MSA outperforms all other
methods most frequently. GMDH breaks the efficient market hypothesis more frequently (75%) than other
methods. The change of the relative predictability during Covid-19 is small with some increase of the advantage
of the investigated methods over a benchmark. We find that the market has been less efficient during Covid-19,
however, there are no huge differences in prediction performances before and during the Covid-19 period.

Keywords: SVM, LSTM, CDS spreads, GMDH, Markov switching autoregression, COVID-19

Publication based on the results of: Statistical Analysis of Investment Strategy Risk-Return Effectiveness (2022)