A Nonparametric Test of a Strong Leverage Hypothesis

89 Pages Posted: 13 Sep 2012 Last revised: 26 May 2015

See all articles by Oliver B. Linton

Oliver B. Linton

University of Cambridge

Yoon-Jae Whang

Seoul National University - School of Economics

Yumin Yen

Academia Sinica

Date Written: May 25, 2015

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

JEL Classification: C14, C15

Suggested Citation

Linton, Oliver B. and Whang, Yoon-Jae and Yen, Yumin, A Nonparametric Test of a Strong Leverage Hypothesis (May 25, 2015). Available at SSRN: https://ssrn.com/abstract=2145341 or http://dx.doi.org/10.2139/ssrn.2145341

Oliver B. Linton (Contact Author)

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Yoon-Jae Whang

Seoul National University - School of Economics ( email )

San 56-1, Silim-dong, Kwanak-ku
Seoul 151-742
Korea
+82 2 80 6362 (Phone)
+82 2 86 4231 (Fax)

HOME PAGE: http://plaza.snu.ac.kr/~whang

Yumin Yen

Academia Sinica ( email )

Nankang
Taipei, 11529
Taiwan

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