A nonparametric test of a strong leverage hypothesis

Oliver Linton, Yoon-Jae Whang and Yu-Min Yen

Journal of Econometrics, 2016, vol. 194, issue 1, 153-186

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C14 C15 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Related works:
Working Paper: A nonparametric test of a strong leverage hypothesis (2013)
Working Paper: A nonparametric test of a strong leverage hypothesis (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:194:y:2016:i:1:p:153-186

DOI: 10.1016/j.jeconom.2016.02.018

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