Volatility of Volatility Estimation: Central Limit Theorems for the Fourier Transform Estimator and Empirical Study of the Daily Time Series Stylized Facts*
Giacomo Toscano,
Giulia Livieri,
Maria Elvira and
Stefano Marmi
Journal of Financial Econometrics, 2024, vol. 22, issue 1, 252-296
Abstract:
We study the asymptotic normality of two feasible estimators of the integrated volatility of volatility based on the Fourier methodology, which does not require the pre-estimation of the spot volatility. We show that the bias-corrected estimator reaches the optimal rate n1/4, while the estimator without bias-correction has a slower convergence rate and a smaller asymptotic variance. Additionally, we provide simulation results that support the theoretical asymptotic distribution of the rate-efficient estimator and show the accuracy of the latter in comparison with a rate-optimal estimator based on the pre-estimation of the spot volatility. Finally, using the rate-optimal Fourier estimator, we reconstruct the series of the daily volatility of volatility of the S&P500 and EUROSTOXX50 indices over long samples and provide novel insight into the existence of stylized facts about the volatility of volatility dynamics.
Keywords: central limit theorem; Fourier analysis; non-parametric estimation; stochastic volatility; volatility of volatility (search for similar items in EconPapers)
JEL-codes: C14 C58 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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