A Nonparametric Test of a Strong Leverage Hypothesis
89 Pages Posted: 13 Sep 2012 Last revised: 26 May 2015
Date Written: May 25, 2015
The so-called leverage hypothesis is that negative shocks to prices/returns affect volatility more than equal positive shocks. Whether this is attributable to changing financial leverage is still subject to dispute but the terminology is in wide use. There are many tests of the leverage hypothesis using discrete time data. These typically involve fitting of a general parametric or semiparametric model to conditional volatility and then testing the implied restrictions on parameters or curves. We propose an alternative way of testing this hypothesis using realized volatility as an alternative direct nonparametric measure. Our null hypothesis is of conditional distributional dominance and so is much stronger than the usual hypotheses considered previously. We implement our test on individual stocks and a stock index using intraday data over a long span. We find only very weak evidence against our hypothesis.
Keywords: Distribution function, Leverage Effect, Gaussian Process
JEL Classification: C14, C15
Suggested Citation: Suggested Citation