{"title":"考虑联邦基金利率的时空信息传递网络方法,用于可解释的资产波动预测框架","authors":"","doi":"10.1016/j.iref.2024.103562","DOIUrl":null,"url":null,"abstract":"<div><p>This study explores the complex interdependencies among five major financial assets—S&P 500, Bitcoin, Crude Oil, Gold, and USD/EUR—from April 2015 to September 2022, emphasizing the importance of understanding global financial dynamics for robust financial management. We quantitatively analyze daily causal relationships using conditional transfer entropy, a method that surpasses traditional correlation analyses. By incorporating the effective federal funds rate into our models, we enhance predictive accuracy and account for monetary policy impacts, ensuring our findings are relevant to current economic conditions. Our results reveal significant causal networks, providing key insights into asset interdependencies that support advanced hedging strategies and effective diversification. This research improves prediction models through the innovative use of network-based features and offers practical strategies for managing multinational financial assets, with relevance across various economic scenarios.</p></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A temporal information transfer network approach considering federal funds rate for an interpretable asset fluctuation prediction framework\",\"authors\":\"\",\"doi\":\"10.1016/j.iref.2024.103562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study explores the complex interdependencies among five major financial assets—S&P 500, Bitcoin, Crude Oil, Gold, and USD/EUR—from April 2015 to September 2022, emphasizing the importance of understanding global financial dynamics for robust financial management. We quantitatively analyze daily causal relationships using conditional transfer entropy, a method that surpasses traditional correlation analyses. By incorporating the effective federal funds rate into our models, we enhance predictive accuracy and account for monetary policy impacts, ensuring our findings are relevant to current economic conditions. Our results reveal significant causal networks, providing key insights into asset interdependencies that support advanced hedging strategies and effective diversification. This research improves prediction models through the innovative use of network-based features and offers practical strategies for managing multinational financial assets, with relevance across various economic scenarios.</p></div>\",\"PeriodicalId\":14444,\"journal\":{\"name\":\"International Review of Economics & Finance\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Review of Economics & Finance\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1059056024005549\",\"RegionNum\":2,\"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":"International Review of Economics & Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1059056024005549","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
A temporal information transfer network approach considering federal funds rate for an interpretable asset fluctuation prediction framework
This study explores the complex interdependencies among five major financial assets—S&P 500, Bitcoin, Crude Oil, Gold, and USD/EUR—from April 2015 to September 2022, emphasizing the importance of understanding global financial dynamics for robust financial management. We quantitatively analyze daily causal relationships using conditional transfer entropy, a method that surpasses traditional correlation analyses. By incorporating the effective federal funds rate into our models, we enhance predictive accuracy and account for monetary policy impacts, ensuring our findings are relevant to current economic conditions. Our results reveal significant causal networks, providing key insights into asset interdependencies that support advanced hedging strategies and effective diversification. This research improves prediction models through the innovative use of network-based features and offers practical strategies for managing multinational financial assets, with relevance across various economic scenarios.
期刊介绍:
The International Review of Economics & Finance (IREF) is a scholarly journal devoted to the publication of high quality theoretical and empirical articles in all areas of international economics, macroeconomics and financial economics. Contributions that facilitate the communications between the real and the financial sectors of the economy are of particular interest.