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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1016/j.eswa.2022.116553
|2 doi
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|a DE-627
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|a eng
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|a Vukovic, Darko B
|e verfasserin
|4 aut
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|a Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 21.12.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Elsevier Ltd. All rights reserved.
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|a 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
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|a Journal Article
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|a CDS spreads
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|a Covid-19
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|a GMDH
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| 650 |
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|a LSTM
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| 650 |
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|a Markov switching autoregression
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| 650 |
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|a SVM
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| 700 |
1 |
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|a Romanyuk, Kirill
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ivashchenko, Sergey
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Grigorieva, Elena M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Expert systems with applications
|d 1999
|g 194(2022) vom: 15. Mai, Seite 116553
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnns
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| 773 |
1 |
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|g volume:194
|g year:2022
|g day:15
|g month:05
|g pages:116553
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|u http://dx.doi.org/10.1016/j.eswa.2022.116553
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