Maximum Drawdown Distributions: The Cross-Asset Dimension
| العنوان: | Maximum Drawdown Distributions: The Cross-Asset Dimension |
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| المؤلفون: | Peter Warken, Angelina Kostyrina |
| المصدر: | The Journal of Investing. 30:7-21 |
| بيانات النشر: | Pageant Media US, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Bootstrapping, Management of Technology and Innovation, Strategy and Management, Risk measure, Econometrics, Position (finance), Drawdown (economics), Asset allocation, Asset (economics), Tail risk, Portfolio optimization, Finance |
| الوصف: | Potential severe drawdowns are a central concern of investors and pose a risk often inadequately considered in the risk profiling or portfolio optimization process. In this article, conditional expected drawdowns are extended from a multi-asset perspective by introducing the conditional expected cross-maximum drawdown measure. The dimensions of magnitude and time are combined to describe tail risk dynamics across asset classes. Beyond extending the risk analytics toolbox, approaches are introduced to explicitly and computational efficiently incorporate this perspective in the optimization process. This puts investors in the position to significantly improve the tails of the maximum drawdown distribution of their strategic asset allocation. Key Findings ▪ The understanding of maximum drawdown distributions is extended from a multi-asset perspective to address a central concern of investors. ▪ A framework to estimate and analyze the dynamics across asset classes is established by using the introduced risk measure and bootstrapping simulations. ▪ Applications in portfolio optimization highlight the fact that investors can significantly increase resilience and improve the risk-adjusted returns of their strategic asset allocation. |
| تدمد: | 2168-8613 1068-0896 |
| DOI: | 10.3905/joi.2021.1.194 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::a91ac5a9deb56873ede0b8683ad089e8 https://doi.org/10.3905/joi.2021.1.194 |
| رقم الانضمام: | edsair.doi...........a91ac5a9deb56873ede0b8683ad089e8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21688613 10680896 |
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| DOI: | 10.3905/joi.2021.1.194 |