英文标题：
《Biased Risk Parity with Fractal Model of Risk》
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作者：
Sergey Kamenshchikov, Ilia Drozdov
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最新提交年份：
2017
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英文摘要：
For the past two decades investors have observed long memory and highly correlated behavior of asset classes that does not fit into the framework of Modern Portfolio Theory. Custom correlation and standard deviation estimators consider normal distribution of returns and market efficiency hypothesis. It forced investors to search more universal instruments of tail risk protection. One of the possible solutions is a naive risk parity strategy, which avoids estimation of expected returns and correlations. The authors develop the idea further and propose a fractal distribution of returns as a core. This class of distributions is more general as it does not imply strict limitations on risk evolution. The proposed model allows for modifying a rule for volatility estimation, thus, enhancing its explanatory power. It turns out that the latter improves the performance metrics of an investment portfolio over the ten year period. The fractal model of volatility plays a significant protective role during the periods of market abnormal drawdowns. Consequently, it may be useful for a wide range of asset managers which incorporate innovative risk models into globally allocated portfolios.
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中文摘要：
在过去二十年中，投资者观察到资产类别的长期记忆和高度相关行为，这不符合现代投资组合理论的框架。自定义相关和标准差估计考虑了收益的正态分布和市场效率假设。这迫使投资者寻找更通用的尾部风险保护工具。可能的解决方案之一是一种简单的风险平价策略，它避免了对预期收益和相关性的估计。作者进一步发展了这一想法，并提出了以分形回报分布为核心的方法。这类分布更为普遍，因为它并不意味着对风险演变的严格限制。该模型允许修改波动率估计规则，从而增强其解释力。结果表明，后者在十年期间改善了投资组合的绩效指标。波动的分形模型在市场异常下跌期间起着重要的保护作用。因此，它可能对将创新风险模型纳入全球配置投资组合的广泛资产管理公司有用。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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