A Nonparametric Test of a Strong Leverage Hypothesis

The so-called leverage hypothesis is that negative shocks to prices/returns aect volatility more than equal positive shocks. Whether this is attributable to changing nancial 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 tting 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 a number of stock return datasets using intraday data over a long span. We nd powerful evidence in favour of our hypothesis.