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A nonparametric test of a strong leverage hypothesis

Authors: Oliver Linton , Yoon-Jae Whang and Yu-Min Yen
Date: 01 July 2013
Type: cemmap Working Paper, CWP28/13
DOI: 10.1920/wp.cem.2013.2813

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 the 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 realised 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 or our hypothesis. Download full version
Now published:
Oliver Linton, Yoon-Jae Whang and Yu-Min Yen September 2016, A nonparametric test of a strong leverage hypothesis, Journal Article, Elsevier
Previous version:
Oliver Linton, Yoon-Jae Whang and Yu-Min Yen September 2012, A nonparametric test of the leverage hypothesis, cemmap Working Paper, CWP24/12

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