We propose a set of dependence measures that are non-linear, local, invariant to a wide range of transformations on the marginals, can show tail and risk asymmetries, are always well-defined, are easy to estimate and can be used on any dataset. We propose a nonparametric estimator and prove its consistency and asymptotic normality. Thereby we significantly improve on existing (extreme) dependence measures used in asset pricing and statistics. To show practical utility, we use these measures on high-frequency stock return data around market distress events such as the 2010 Flash Crash and during the GFC. Contrary to ubiquitously used correlations we find that our measures clearly show tail asymmetry, non-linearity, lack of diversification and endogenous buildup of risks present during these distress events. Additionally, our measures anticipate large (joint) losses during the Flash Crash while also anticipating the bounce back and flagging the subsequent market fragility. Our findings have implications for risk management, portfolio construction and hedging at any frequency.

翻译：我们提出了一组依赖度量，这些度量具有非线性、局部性、对边缘分布广泛变换的不变性，能够捕捉尾部与风险不对称性，始终定义明确、易于估计，并可适用于任何数据集。我们提出了一种非参数估计量，并证明了其一致性和渐近正态性。由此，我们显著改进了资产定价与统计学中现有的（极端）依赖度量。为展示其实用价值，我们将这些度量应用于市场困境事件（如2010年闪电崩盘及全球金融危机期间）的高频股票收益率数据。与普遍使用的相关性不同，我们发现这些度量清晰揭示了这些困境事件期间存在的尾部不对称性、非线性、缺乏分散化以及风险的内生积累。此外，这些度量能预测闪电崩盘期间的大额（联合）损失，同时预见市场反弹并标记随后出现的市场脆弱性。我们的研究结果对任意频率下的风险管理、投资组合构建及对冲具有启示意义。