{"title":"预测加密货币的风险价值","authors":"Michael Michaelides, Niraj Poudyal","doi":"10.1111/irfi.70029","DOIUrl":null,"url":null,"abstract":"<p>Value-at-Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time-heterogeneous Student's <i>t</i> autoregressive model outperforms standard models commonly used by practitioners.</p>","PeriodicalId":46664,"journal":{"name":"International Review of Finance","volume":"25 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/irfi.70029","citationCount":"0","resultStr":"{\"title\":\"Forecasting value-at-risk for cryptocurrencies\",\"authors\":\"Michael Michaelides, Niraj Poudyal\",\"doi\":\"10.1111/irfi.70029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Value-at-Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time-heterogeneous Student's <i>t</i> autoregressive model outperforms standard models commonly used by practitioners.</p>\",\"PeriodicalId\":46664,\"journal\":{\"name\":\"International Review of Finance\",\"volume\":\"25 3\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/irfi.70029\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Review of Finance\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/irfi.70029\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Finance","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/irfi.70029","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Value-at-Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time-heterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.
期刊介绍:
The International Review of Finance (IRF) publishes high-quality research on all aspects of financial economics, including traditional areas such as asset pricing, corporate finance, market microstructure, financial intermediation and regulation, financial econometrics, financial engineering and risk management, as well as new areas such as markets and institutions of emerging market economies, especially those in the Asia-Pacific region. In addition, the Letters Section in IRF is a premium outlet of letter-length research in all fields of finance. The length of the articles in the Letters Section is limited to a maximum of eight journal pages.