We study the finite sample properties of the Fourier estimator of the integrated leverage effectin the presence of microstructure noise contamination. Our estimation strategy is related to a measure of the contemporaneous correlation between financial returns and their volatility increments . We do not prior assume that the aforementioned correlation is constant, as mainly done in the literature. We instead consider it as a stochastic process. In this framework, we show that the Fourier estimator is asymptotically unbiased but its mean squared error diverges when noisy high-frequency data are in use. This drawback of the estimator is further analyzed in a simulation study where a feasible estimation strategy is developed to tackle this problem. The paper concludes with an empirical study on the leverage effect patterns estimated using high-frequency data for the S P 500 futures between January 2007 and December 2008.
We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up. We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002). We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination. When applying our methodology to real data, our results provide evidence of the presence of the stochastic leverage effect . 
R
study the finite sample properties of the Fourier estimator of the integrated leverage effectin the presence of microstructure noise contamination. Our estimation strategy is related to a measure of the contemporaneous correlation between financial
analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the
R
volatility increments
related volatility increments, in a stochastic volatility model set-up. We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002). We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination. When applying our methodology to real data, our results provide evidence of the presence of the stochastic leverage effect
D
We do not prior assume that the aforementioned correlation is constant, as mainly done in the literature. We instead consider it as a stochastic process. In this framework, we show that the Fourier estimator is asymptotically unbiased but its mean squared error diverges when noisy high-frequency data are in use. This drawback of the estimator is further analyzed in a simulation study where a feasible estimation strategy is developed to tackle this problem. The paper concludes with an empirical study on the leverage effect patterns estimated using high-frequency data for the S
D
P 500 futures between January 2007 and December 2008.