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A nonparametric test of the leverage hypothesis

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  • Oliver Linton

    ()
    (Institute for Fiscal Studies and Cambridge University)

  • Yoon-Jae Whang

    (Institute for Fiscal Studies and Seoul National University)

  • Yu-Min Yen

Abstract

The so-called leverage hypothesis is that negative shocks to prices/returns aff ect 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 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 of our hypothesis.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP24/12.

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Date of creation: Sep 2012
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Handle: RePEc:ifs:cemmap:24/12

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Keywords: Distribution Function; Leverage Effect; Gaussian Process;

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  1. Linton, Oliver & Song, Kyungchul & Whang, Yoon-Jae, 2010. "An improved bootstrap test of stochastic dominance," Journal of Econometrics, Elsevier, vol. 154(2), pages 186-202, February.
  2. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Papers 2003-W21, Economics Group, Nuffield College, University of Oxford.
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