{"title":"估计铜的风险价值","authors":"Konstantinos Gkillas , Christoforos Konstantatos , Spyros Papathanasiou , Mark Wohar","doi":"10.1016/j.jcomm.2023.100351","DOIUrl":null,"url":null,"abstract":"<div><p><span>We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional Autoregressive Value at Risk model specifications. The best model is selected using the Model Confidence Set approach. This approach provides a superior set of models by testing the null hypothesis of equal </span>predictive ability. The findings suggest that the EGARCH model outperforms the rest of the models for the copper commodity under investigation.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"32 ","pages":"Article 100351"},"PeriodicalIF":3.7000,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of value at risk for copper\",\"authors\":\"Konstantinos Gkillas , Christoforos Konstantatos , Spyros Papathanasiou , Mark Wohar\",\"doi\":\"10.1016/j.jcomm.2023.100351\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional Autoregressive Value at Risk model specifications. The best model is selected using the Model Confidence Set approach. This approach provides a superior set of models by testing the null hypothesis of equal </span>predictive ability. The findings suggest that the EGARCH model outperforms the rest of the models for the copper commodity under investigation.</p></div>\",\"PeriodicalId\":45111,\"journal\":{\"name\":\"Journal of Commodity Markets\",\"volume\":\"32 \",\"pages\":\"Article 100351\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Commodity Markets\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405851323000417\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Commodity Markets","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405851323000417","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional Autoregressive Value at Risk model specifications. The best model is selected using the Model Confidence Set approach. This approach provides a superior set of models by testing the null hypothesis of equal predictive ability. The findings suggest that the EGARCH model outperforms the rest of the models for the copper commodity under investigation.
期刊介绍：
The purpose of the journal is also to stimulate international dialog among academics, industry participants, traders, investors, and policymakers with mutual interests in commodity markets. The mandate for the journal is to present ongoing work within commodity economics and finance. Topics can be related to financialization of commodity markets; pricing, hedging, and risk analysis of commodity derivatives; risk premia in commodity markets; real option analysis for commodity project investment and production; portfolio allocation including commodities; forecasting in commodity markets; corporate finance for commodity-exposed corporations; econometric/statistical analysis of commodity markets; organization of commodity markets; regulation of commodity markets; local and global commodity trading; and commodity supply chains. Commodity markets in this context are energy markets (including renewables), metal markets, mineral markets, agricultural markets, livestock and fish markets, markets for weather derivatives, emission markets, shipping markets, water, and related markets. This interdisciplinary and trans-disciplinary journal will cover all commodity markets and is thus relevant for a broad audience. Commodity markets are not only of academic interest but also highly relevant for many practitioners, including asset managers, industrial managers, investment bankers, risk managers, and also policymakers in governments, central banks, and supranational institutions.
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