{"title":"Stock-Bond Return Correlations: Moving Away from ‘One-Frequency-Fits-All’ by Extending the DCC-MIDAS Approach","authors":"Anne-Florence Allard, Leonardo Iania, Kristien Smedts","doi":"10.2139/ssrn.3190071","DOIUrl":null,"url":null,"abstract":"This paper explores the determinants of U.S. stock-bond correlations estimated at various frequencies. For this purpose, the two-component DCC-MIDAS model of correlation Colacito, Engle & Ghysels (2011) is used and extended to incorporate a third correlation frequency component. Subsequently, macroeconomic and financial variables are studied as determinants of each component. We show that the daily correlation component is driven by financial market factors, while the monthly component is more influenced by macroeconomic factors. Finally, the yearly component is determined by funding opportunities in the economy. These results are important as they show that different correlation components and determinants should be considered for different investment horizons.","PeriodicalId":130177,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Asset Pricing (Topic)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometric Modeling: Capital Markets - Asset Pricing (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3190071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper explores the determinants of U.S. stock-bond correlations estimated at various frequencies. For this purpose, the two-component DCC-MIDAS model of correlation Colacito, Engle & Ghysels (2011) is used and extended to incorporate a third correlation frequency component. Subsequently, macroeconomic and financial variables are studied as determinants of each component. We show that the daily correlation component is driven by financial market factors, while the monthly component is more influenced by macroeconomic factors. Finally, the yearly component is determined by funding opportunities in the economy. These results are important as they show that different correlation components and determinants should be considered for different investment horizons.