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We propose a set of dependence measures that are non-linear, local, invariantto a wide range of transformations on the marginals, can show tail and riskasymmetries, are always well-defined, are easy to estimate and can be used onany dataset. We propose a nonparametric estimator and prove its consistency andasymptotic normality. Thereby we significantly improve on existing (extreme)dependence measures used in asset pricing and statistics. To show practicalutility, we use these measures on high-frequency stock return data aroundmarket distress events such as the 2010 Flash Crash and during the GFC.Contrary to ubiquitously used correlations we find that our measures clearlyshow tail asymmetry, non-linearity, lack of diversification and endogenousbuildup of risks present during these distress events. Additionally, ourmeasures anticipate large (joint) losses during the Flash Crash while alsoanticipating the bounce back and flagging the subsequent market fragility. Ourfindings have implications for risk management, portfolio construction andhedging at any frequency.

New general dependence measures: construction, estimation and application to high-frequency stock returns