skip to content

Faculty of Economics

Journal Cover

Linton, O., Whang, Y.-J. and Yen, Y.-M.

A nonparametric test of a strong leverage hypothesis

Journal of Econometrics

Vol. 194(1) pp. 153-186 (2016)

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 Codes: C14, C15

Author links: Oliver Linton  

Publisher's Link:

Papers and Publications

Recent Publications

Ambrus, A. and Elliott, M. Investments in Social Ties, Risk Sharing, and Inequality Review of Economic Studies [2021]

Huffman, D., Raymond, C. and Shvets, J. Persistent Overconfidence and Biased Memory: Evidence from Managers American Economic Review [2022]

Bodenstein, M., Corsetti G. and Guerrieri, L. Social Distancing and Supply Disruptions in a Pandemic Quantitative Economics [2022]

Mueller, H. and Rauh, C. The Hard Problem of Prediction for Conflict Prevention Journal of the European Economic Association [2022]