A nonparametric test of a strong leverage hypothesis

• Published in: Journal of econometrics Vol. 194; no. 1; pp. 153 - 186
• Main Authors: Linton, Oliver, Whang, Yoon-Jae, Yen, Yu-Min
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2016; Elsevier Sequoia S.A
• Subjects: Distribution function; econometric models; Econometrics; economic analysis; economic theory; Financial leverage; Gaussian process; Hypotheses; Hypothesis testing; Leverage effect; prices; Rates of return; Stock prices; Studies; terminology; Volatility; Distribution function; Gaussian process; Leverage effect; C15; C14
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2016.02.018

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.