{"title":"回报环境下的可持续提款率:时变贝叶斯分析","authors":"Kevin Khang, D. Pakula, Andrew S. Clarke","doi":"10.3905/jor.2022.10.2.070","DOIUrl":null,"url":null,"abstract":"As investors depend more on their investment portfolios to generate income in retirement, financial advisors have developed rules of thumb to estimate a portfolio’s sustainable withdrawal rate (SWR). The most famous heuristic is Bengen’s “4% rule.” The strength of these rules is their ease of implementation. Among their limitations is their insensitivity to long-term variation in return environments. We borrow a statistical technique from the macroeconomics literature—a Bayesian vector autoregression with time-varying parameters—to model the impact of changing return dynamics on SWRs. We first illustrate how changes in stock-bond correlation, return and inflation volatilities, and returns have affected historical SWRs. Building on the consensus forecast, we then use these insights to identify 2.8% to 3.3% to be a relevant SWR for those retiring in today’s evironment characterized by low prospective returns and inflationary uncertainties.","PeriodicalId":36429,"journal":{"name":"Journal of Retirement","volume":"10 1","pages":"70 - 88"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sustainable Withdrawal Rates by Return Environment: A Time-Varying Bayesian Analysis\",\"authors\":\"Kevin Khang, D. Pakula, Andrew S. Clarke\",\"doi\":\"10.3905/jor.2022.10.2.070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As investors depend more on their investment portfolios to generate income in retirement, financial advisors have developed rules of thumb to estimate a portfolio’s sustainable withdrawal rate (SWR). The most famous heuristic is Bengen’s “4% rule.” The strength of these rules is their ease of implementation. Among their limitations is their insensitivity to long-term variation in return environments. We borrow a statistical technique from the macroeconomics literature—a Bayesian vector autoregression with time-varying parameters—to model the impact of changing return dynamics on SWRs. We first illustrate how changes in stock-bond correlation, return and inflation volatilities, and returns have affected historical SWRs. Building on the consensus forecast, we then use these insights to identify 2.8% to 3.3% to be a relevant SWR for those retiring in today’s evironment characterized by low prospective returns and inflationary uncertainties.\",\"PeriodicalId\":36429,\"journal\":{\"name\":\"Journal of Retirement\",\"volume\":\"10 1\",\"pages\":\"70 - 88\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Retirement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3905/jor.2022.10.2.070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Retirement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3905/jor.2022.10.2.070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
Sustainable Withdrawal Rates by Return Environment: A Time-Varying Bayesian Analysis
As investors depend more on their investment portfolios to generate income in retirement, financial advisors have developed rules of thumb to estimate a portfolio’s sustainable withdrawal rate (SWR). The most famous heuristic is Bengen’s “4% rule.” The strength of these rules is their ease of implementation. Among their limitations is their insensitivity to long-term variation in return environments. We borrow a statistical technique from the macroeconomics literature—a Bayesian vector autoregression with time-varying parameters—to model the impact of changing return dynamics on SWRs. We first illustrate how changes in stock-bond correlation, return and inflation volatilities, and returns have affected historical SWRs. Building on the consensus forecast, we then use these insights to identify 2.8% to 3.3% to be a relevant SWR for those retiring in today’s evironment characterized by low prospective returns and inflationary uncertainties.