{"title":"如何飞到安全的地方而不多付机票钱","authors":"Tomasz Kaczmarek, Przemysław Grobelny","doi":"10.18559/ebr.2023.2.738","DOIUrl":null,"url":null,"abstract":"Abstract For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"431 1","pages":"160 - 183"},"PeriodicalIF":1.2000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How to fly to safety without overpaying for the ticket\",\"authors\":\"Tomasz Kaczmarek, Przemysław Grobelny\",\"doi\":\"10.18559/ebr.2023.2.738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).\",\"PeriodicalId\":41557,\"journal\":{\"name\":\"Economics and Business Review\",\"volume\":\"431 1\",\"pages\":\"160 - 183\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Economics and Business Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18559/ebr.2023.2.738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics and Business Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18559/ebr.2023.2.738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
摘要
对于大多数积极的投资者来说,国库券提供了分散投资,从而降低了投资组合的风险。在风险升高的时候,政府的这些特征变得特别可取,这种风险以“逃向安全”(FTS)现象的形式出现。为政府提供的FTS在市场动荡期间提供了一个避难所,对减少投资组合的风险特别有益。然而,如果美国国债的预期回报不令人满意,导致投资者不愿增持债券,那该怎么办?本文提出了一种深度目标波动率股票-债券配置(Deep Target Volatility equity - bond Allocation, DTVEBA)方法来解决这一问题。该策略由最先进的循环神经网络(RNN)驱动,该网络可以预测第二天的市场波动。一项为期12年的样本外分析发现,使用DTVEBA,投资者可能会将国债配置减少两(三)倍,以获得相同的夏普(卡尔马)比率,并比标准普尔500指数高出43%(115%)。
How to fly to safety without overpaying for the ticket
Abstract For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).