Observing Cryptocurrencies through Robust Anomaly Scores
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatilit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/11/1643 |
| _version_ | 1797465404289843200 |
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| author | Geumil Bae Jang Ho Kim |
| author_facet | Geumil Bae Jang Ho Kim |
| author_sort | Geumil Bae |
| collection | DOAJ |
| description | The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis. |
| first_indexed | 2024-03-09T18:20:57Z |
| format | Article |
| id | doaj.art-c15859af16884ffbaaf92223ff45414f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T18:20:57Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c15859af16884ffbaaf92223ff45414f2023-11-24T08:18:30ZengMDPI AGEntropy1099-43002022-11-012411164310.3390/e24111643Observing Cryptocurrencies through Robust Anomaly ScoresGeumil Bae0Jang Ho Kim1Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, KoreaDepartment of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si 17104, KoreaThe cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.https://www.mdpi.com/1099-4300/24/11/1643cryptocurrencyanomaly scoreMahalanobis distanceminimum covariance determinantshrinkage estimators |
| spellingShingle | Geumil Bae Jang Ho Kim Observing Cryptocurrencies through Robust Anomaly Scores Entropy cryptocurrency anomaly score Mahalanobis distance minimum covariance determinant shrinkage estimators |
| title | Observing Cryptocurrencies through Robust Anomaly Scores |
| title_full | Observing Cryptocurrencies through Robust Anomaly Scores |
| title_fullStr | Observing Cryptocurrencies through Robust Anomaly Scores |
| title_full_unstemmed | Observing Cryptocurrencies through Robust Anomaly Scores |
| title_short | Observing Cryptocurrencies through Robust Anomaly Scores |
| title_sort | observing cryptocurrencies through robust anomaly scores |
| topic | cryptocurrency anomaly score Mahalanobis distance minimum covariance determinant shrinkage estimators |
| url | https://www.mdpi.com/1099-4300/24/11/1643 |
| work_keys_str_mv | AT geumilbae observingcryptocurrenciesthroughrobustanomalyscores AT janghokim observingcryptocurrenciesthroughrobustanomalyscores |