{"title":"Deconstructing the Yield Curve","authors":"Richard K. Crump, Nikolay Gospodinov","doi":"10.2139/ssrn.3368414","DOIUrl":null,"url":null,"abstract":"We investigate the factor structure of the term structure of interest rates and argue that characterizing the minimal dimension of the data generating process is more challenging than currently appreciated. As a result, inference procedures for yield curve models that commit to a parsimoniously parameterized factor structure may be omitting important information about the underlying true factor space. To circumvent these difficulties, we introduce a novel nonparametric bootstrap that is robust to general forms of time and cross-sectional dependence and conditional heteroskedasticity of unknown form. We show that our bootstrap procedure is asymptotically valid and exhibits excellent finite-sample properties in simulations. We demonstrate the applicability of our results in two empirical exercises: first, we show that measures of equity market tail risk and the state of the macroeconomy predict bond returns beyond the level or slope of the yield curve; second, we provide a bootstrap-based bias correction and confidence intervals for the probability of recession based on the shape of the yield curve. Our results apply more generally to all assets with a finite maturity structure.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3368414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We investigate the factor structure of the term structure of interest rates and argue that characterizing the minimal dimension of the data generating process is more challenging than currently appreciated. As a result, inference procedures for yield curve models that commit to a parsimoniously parameterized factor structure may be omitting important information about the underlying true factor space. To circumvent these difficulties, we introduce a novel nonparametric bootstrap that is robust to general forms of time and cross-sectional dependence and conditional heteroskedasticity of unknown form. We show that our bootstrap procedure is asymptotically valid and exhibits excellent finite-sample properties in simulations. We demonstrate the applicability of our results in two empirical exercises: first, we show that measures of equity market tail risk and the state of the macroeconomy predict bond returns beyond the level or slope of the yield curve; second, we provide a bootstrap-based bias correction and confidence intervals for the probability of recession based on the shape of the yield curve. Our results apply more generally to all assets with a finite maturity structure.