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Together, European metropolises represent a very substantial public procurement market of about €10bn per year and this has a leverage effect on the private sector that very often aligns its own requirements with the public sector.
This building has a leverage effect on Asian economies.
We found little evidence for a leverage effect (leverage = zero).
It was revealed that most of the models rejected the existence of a leverage effect, except for those with volatility breaks.
Thus, when γ > 0, the increase in volatility due to bad news is greater than that for good news, and we can conclude that there is a leverage effect.
Meanwhile, the negative significant value of γ indicates the existence of a leverage effect in the opposite direction: positive past values of ε t increase volatility more than negative past values of the same magnitude.
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Therefore, in this case, we can identify either a negative leverage effect when there is none (the series is truly a Gaussian white noise) or a much more negative leverage effect than the actual one (as in the case of the EGARCH model).
Consequently, we could wrongly identify asymmetries in a series that is actually white noise or we could identify a positive leverage effect when it is truly negative as in the EGARCH process.
Findings in the form of backward multipliers suggest that R&D subsidies have stimulated a substantial leverage effect.
Higher signal intensities have a larger leverage effect on the slope than lower ones, which is given by the model.
Thus, if a heteroscedastic time series with leverage effect is contaminated by a large single outlier, the detection of genuine leverage effect will be difficult, as it was the detection of genuine heteroscedasticity; see Carnero et al.
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