{"title":"Pre-Shrinkage: Improved Volatility Forecasting Using Biased Time-Series","authors":"R. Quaedvlieg","doi":"10.2139/ssrn.3716425","DOIUrl":null,"url":null,"abstract":"We propose to model and forecast realized covariances by estimating reduced form models on 'pre-shrunk' time-series. By adapting established linear and non-linear shrinkage techniques to high-frequency volatility estimates we construct an alternative time-series that is biased, but offers an expected Frobenius norm improvement with respect to the latent covariance matrix. Both parameter estimates and forecasts are based on the pre-shrunk series. We document statistically and economically significant forecast improvements based on statistical loss functions with respect to both the standard and shrunk realized covariance measures, for cross-sectional dimensions ranging from one to over a hundred. The forecasts also lead to improved global minimum variance portfolios, which do not inherently favour either series. The pre-shrunk models compare favourably to alternative measurement-error alleviating techniques.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Management & Analysis in Financial Institutions eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3716425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We propose to model and forecast realized covariances by estimating reduced form models on 'pre-shrunk' time-series. By adapting established linear and non-linear shrinkage techniques to high-frequency volatility estimates we construct an alternative time-series that is biased, but offers an expected Frobenius norm improvement with respect to the latent covariance matrix. Both parameter estimates and forecasts are based on the pre-shrunk series. We document statistically and economically significant forecast improvements based on statistical loss functions with respect to both the standard and shrunk realized covariance measures, for cross-sectional dimensions ranging from one to over a hundred. The forecasts also lead to improved global minimum variance portfolios, which do not inherently favour either series. The pre-shrunk models compare favourably to alternative measurement-error alleviating techniques.