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

TL;DR

This paper introduces new non-linear, local dependence measures that are invariant, well-defined, and easy to estimate, demonstrating their effectiveness on high-frequency stock data during market distress events.

Contribution

It develops a novel class of dependence measures with proven consistency and asymptotic normality, improving on existing methods for asset pricing and risk analysis.

Findings

Measures reveal tail asymmetry and non-linearity during crises

Detect endogenous risk buildup and diversification failure

Anticipate large joint losses and market rebounds

Abstract

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.