利用对数周期幂律模型和小波分析检测加密货币价格泡沫

IF 4.6 3区管理学 Q1 BUSINESS

IEEE Transactions on Engineering Management Pub Date : 2024-07-17 DOI:10.1109/TEM.2024.3427647

Junhuan Zhang;Haodong Wang;Jing Chen;Anqi Liu

{"title":"利用对数周期幂律模型和小波分析检测加密货币价格泡沫","authors":"Junhuan Zhang;Haodong Wang;Jing Chen;Anqi Liu","doi":"10.1109/TEM.2024.3427647","DOIUrl":null,"url":null,"abstract":"In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cryptocurrency Price Bubble Detection Using Log-Periodic Power Law Model and Wavelet Analysis\",\"authors\":\"Junhuan Zhang;Haodong Wang;Jing Chen;Anqi Liu\",\"doi\":\"10.1109/TEM.2024.3427647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.\",\"PeriodicalId\":55009,\"journal\":{\"name\":\"IEEE Transactions on Engineering Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Engineering Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10601303/\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10601303/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}

引用次数: 0

摘要

在本文中，我们建立了一种检测和制定加密货币市场价格泡沫的方法。该方法通过违反指数衰减特性来识别异常崩溃。通过小波分析，在这些异常现象中获得泡沫破裂的确认。通过将加密货币价格分解为高频因素和低频因素，我们在时域和频域上区分了价格体系与泡沫和崩溃时期。此外，我们还应用对数周期幂律模型来拟合泡沫的形成。在对 2018 年 5 月 15 日至 2022 年 11 月 28 日期间的八种加密货币--比特币、以太坊、莱特币、Antshares、以太坊经典版、Dash、Monero 和 OmiseGO--的分析中，我们发现了 24 个泡沫。其中一些泡沫呈现出显著而强劲的指数增长模式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Cryptocurrency Price Bubble Detection Using Log-Periodic Power Law Model and Wavelet Analysis

In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Engineering Management 管理科学-工程：工业

CiteScore

10.30

自引率

19.00%

发文量

604

审稿时长

5.3 months

期刊介绍： Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.