Conditional Skewness with Quantile Regression Models: SoFiE Presidential Address and a Tribute to Hal White
Abstract
We study a new class of conditional skewness models based on conditional quantiles regressions. The approach is much inspired by work of Hal White. To handle multiple horizons I consider quantile MIDAS regressions which amount to direct forecasting—as opposed to iterated forecasting—conditional skewness. Using this quantile-based approach I document that the conditional asymmetry of returns varies significantly over time. The asymmetry is most relevant for the characterization of downside risk. Besides empirical evidence, I also report simulation results which highlight the costs associated with mis-specifying downside risk in the presence of conditional skewness.
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- Keyword(s):
- data project 74
- finance 247
https://kenaninstitute.unc.edu/publication/conditional-skewness-with-quantile-regression-models-sofie-presidential-address-and-a-tribute-to-hal-white/
