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Title:A nonparametric test of the leverage hypothesis PDF Logo
Authors:Linton, Oliver
Whang, Yoon-Jae
Yen, Yu-Min
Issue Date:2012
Series/Report no.:cemmap working paper CWP24/12
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 a number of stock return datasets using intraday data over a long span. We find powerful evidence in favour of our hypothesis.
Subjects:Distribution function
Leverage Effect
Gaussian Process
JEL:C14
C15
Persistent Identifier of the first edition:doi:10.1920/wp.cem.2012.2412
Document Type:Working Paper
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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