Stochastic leverage effect in high-frequency data: a Fourier based analysis

TL;DR

This paper introduces a Fourier-based estimator for the stochastic leverage effect in high-frequency financial data, demonstrating its consistency and finite sample properties, and applying it to S&P 500 futures to measure the effect.

Contribution

It presents a novel Fourier coefficient-based estimator for the stochastic leverage effect and proves its consistency, addressing microstructure noise in high-frequency data.

Findings

The estimator is consistent and effective in finite samples.

Application to S&P 500 futures reveals measurable stochastic leverage effect.

Fourier methodology captures high-frequency leverage dynamics.

Abstract

The stochastic leverage effect, defined as the standardized covariation between the returns and their related volatility, is analyzed in a stochastic volatility model set-up. A novel estimator of the effect is defined using a pre-estimation of the Fourier coefficients of the return and the volatility processes. The consistency of the estimator is proven. Moreover, its finite sample properties are studied in the presence of microstructure noise effects. The Fourier methodology is applied to S\&P500 futures prices to investigate the magnitude of the stochastic leverage effect detectable at high-frequency.