{"title":"Frequency-Domain Information for Active Portfolio Management","authors":"Gonçalo Faria, Fabio Verona","doi":"10.2139/ssrn.3527688","DOIUrl":null,"url":null,"abstract":"We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more accurate than those of traditional forecasting methods for both asset classes. When used in the context of active portfolio management, the forecasts based on frequency-domain information lead to better portfolio performances than when using the original time series of the predictors. It produces higher information ratio (0.57 vs 0.45), higher CER gains (1.12% vs 0.81%), and lower maximum drawdown (19.1% vs 19.6%).","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Modeling: Capital Markets - Forecasting eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3527688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more accurate than those of traditional forecasting methods for both asset classes. When used in the context of active portfolio management, the forecasts based on frequency-domain information lead to better portfolio performances than when using the original time series of the predictors. It produces higher information ratio (0.57 vs 0.45), higher CER gains (1.12% vs 0.81%), and lower maximum drawdown (19.1% vs 19.6%).
我们评估使用频域信息进行主动投资组合管理的好处。为此，我们使用一种方法来预测债券风险溢价和股票风险溢价，该方法分离了具有最高预测能力的频率(预测因子)。对这两种资产类别的预测结果都比传统的预测方法更准确。当在主动投资组合管理的环境中使用时，基于频域信息的预测比使用预测者的原始时间序列时产生更好的投资组合绩效。它产生更高的信息比(0.57 vs 0.45)，更高的CER增益(1.12% vs 0.81%)和更低的最大递减(19.1% vs 19.6%)。
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