A Nonparametric Test of a Strong Leverage Hypothesis∗

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. ∗We thank Valentina Corradi, Jean-Marie Dufour, and Haim Levy for helpful comments. R software for carrying out the conditional dominance test is available from the web site www.oliverlinton.me.uk †Department of Economics, University of Cambridge, Austin Robinson Building, Sidgwick Avenue, Cambridge CB3 9DD, United Kingdom. Thanks to the ERC for financial support. Email: [email protected]. ‡Department of Economics, Seoul National University, Seoul 151-742, Korea. Email: [email protected]. Thanks to the National Research Foundation of Korea (NRF-2011-342-B00004 and NRF-2012S1A3A2033467) and Seoul National University for financial supports. §Department of International Business, National Chengchi University, 64, Sec. 2, Zhi-nan Rd., Wenshan, Taipei 116, Taiwan. Email: yyu [email protected]