The 2021 Bitcoin Bubbles and Crashes—Detection and Classification
In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Stats |
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| Online Access: | https://www.mdpi.com/2571-905X/4/4/56 |
| _version_ | 1797500527276195840 |
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| author | Min Shu Ruiqiang Song Wei Zhu |
| author_facet | Min Shu Ruiqiang Song Wei Zhu |
| author_sort | Min Shu |
| collection | DOAJ |
| description | In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study. |
| first_indexed | 2024-03-10T03:05:04Z |
| format | Article |
| id | doaj.art-aea9086d1ad340c88147560c68490f42 |
| institution | Directory Open Access Journal |
| issn | 2571-905X |
| language | English |
| last_indexed | 2024-03-10T03:05:04Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj.art-aea9086d1ad340c88147560c68490f422023-11-23T10:35:04ZengMDPI AGStats2571-905X2021-11-014495097010.3390/stats4040056The 2021 Bitcoin Bubbles and Crashes—Detection and ClassificationMin Shu0Ruiqiang Song1Wei Zhu2Mathematics, Statistics & Computer Science Department, University of Wisconsin-Stout, Menomonie, WI 54751, USADepartment of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI 49931, USADepartment of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY 11794, USAIn this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.https://www.mdpi.com/2571-905X/4/4/56bitcoin bubblelog-periodic power law singularity (LPPLS)LPPLS confidence indicatorcryptocurrencyfinancial bubble and crashmodified Lagrange regularization method |
| spellingShingle | Min Shu Ruiqiang Song Wei Zhu The 2021 Bitcoin Bubbles and Crashes—Detection and Classification Stats bitcoin bubble log-periodic power law singularity (LPPLS) LPPLS confidence indicator cryptocurrency financial bubble and crash modified Lagrange regularization method |
| title | The 2021 Bitcoin Bubbles and Crashes—Detection and Classification |
| title_full | The 2021 Bitcoin Bubbles and Crashes—Detection and Classification |
| title_fullStr | The 2021 Bitcoin Bubbles and Crashes—Detection and Classification |
| title_full_unstemmed | The 2021 Bitcoin Bubbles and Crashes—Detection and Classification |
| title_short | The 2021 Bitcoin Bubbles and Crashes—Detection and Classification |
| title_sort | 2021 bitcoin bubbles and crashes detection and classification |
| topic | bitcoin bubble log-periodic power law singularity (LPPLS) LPPLS confidence indicator cryptocurrency financial bubble and crash modified Lagrange regularization method |
| url | https://www.mdpi.com/2571-905X/4/4/56 |
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