A nonparametric test of a strong leverage hypothesis

Arbeitspapier

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

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP28/13

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Statistical Simulation Methods: General
• Subject: Distribution function ; Leverage Effect ; Gaussian Process

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Linton, Oliver ; Whang, Yoon-Jae ; Yen, Yu-Min
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2013

Further information

• DOI: doi:10.1920/wp.cem.2013.2813
• Handle: http://hdl.handle.net/10419/79535