Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79535
Authors: 
Linton, Oliver
Whang, Yoon-Jae
Yen, Yu-Min
Year of Publication: 
2013
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP28/13
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 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
Subjects: 
Distribution function
Leverage Effect
Gaussian Process
JEL: 
C14
C15
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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