Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning
In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term struct...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2518283 |
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| author | Won Joong Kim Gunho Jung Sun-Yong Choi |
| author_facet | Won Joong Kim Gunho Jung Sun-Yong Choi |
| author_sort | Won Joong Kim |
| collection | DOAJ |
| description | In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country. |
| format | Article |
| id | doaj-art-eaab5cb21370428daece5d76ca528d1e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-eaab5cb21370428daece5d76ca528d1e2025-02-03T05:49:38ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/25182832518283Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine LearningWon Joong Kim0Gunho Jung1Sun-Yong Choi2Department of Industrial and Management Engineering, POSTECH, Gyeongbuk 37673, Republic of KoreaDepartment of Financial Mathematics, Gachon University, Gyeonggi 13120, Republic of KoreaDepartment of Financial Mathematics, Gachon University, Gyeonggi 13120, Republic of KoreaIn this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.http://dx.doi.org/10.1155/2020/2518283 |
| spellingShingle | Won Joong Kim Gunho Jung Sun-Yong Choi Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning Complexity |
| title | Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning |
| title_full | Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning |
| title_fullStr | Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning |
| title_full_unstemmed | Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning |
| title_short | Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning |
| title_sort | forecasting cds term structure based on nelson siegel model and machine learning |
| url | http://dx.doi.org/10.1155/2020/2518283 |
| work_keys_str_mv | AT wonjoongkim forecastingcdstermstructurebasedonnelsonsiegelmodelandmachinelearning AT gunhojung forecastingcdstermstructurebasedonnelsonsiegelmodelandmachinelearning AT sunyongchoi forecastingcdstermstructurebasedonnelsonsiegelmodelandmachinelearning |