Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression.
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ABSTRACT:
SUBMITTER: Vukovic DB
PROVIDER: S-EPMC8782769 | biostudies-literature | 2022 May
REPOSITORIES: biostudies-literature
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Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression.
Vukovic Darko B DB Romanyuk Kirill K Ivashchenko Sergey S Grigorieva Elena M EM
Expert systems with applications 20220122
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. MS ...[more]
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