{"title":"利用GARCH和ARCH模型预测加密货币波动","authors":"Amadeo Christopher, K. Deniswara, B. Handoko","doi":"10.1145/3537693.3537712","DOIUrl":null,"url":null,"abstract":"This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.","PeriodicalId":71902,"journal":{"name":"电子政务","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Forecasting Cryptocurrency Volatility Using GARCH and ARCH Model\",\"authors\":\"Amadeo Christopher, K. Deniswara, B. Handoko\",\"doi\":\"10.1145/3537693.3537712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.\",\"PeriodicalId\":71902,\"journal\":{\"name\":\"电子政务\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"电子政务\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1145/3537693.3537712\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"电子政务","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1145/3537693.3537712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting Cryptocurrency Volatility Using GARCH and ARCH Model
This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.
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