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Seminar of the “D2 Seminar Series” – Florence Center for Data Science
January 20 @ 14:30 - 16:00
Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science!
We are happy to host Giacomo Toscano from the Department of Economics and Management, University of Florence and Gabriele Fiorentini from the Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence.
Speaker: Giacomo Toscano – Department of Economics and Management, University of Florence
Title: “Central limit theorems for the Fourier-transform estimator of the volatility of volatility”
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 , 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 time 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.”
Speaker: Gabriele Fiorentini – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: “Specification tests for non-Gaussian structural vector autoregressions”
Abstract: We propose specification tests for independent component analysis and structural vector autoregressions that assess the assumed cross-sectional independence of the non-Gaussian shocks. Our tests effectively compare their joint cumulative distribution with the product of their marginals at discrete or continuous grids of values for its arguments, the latter yielding a consistent test. We explicitly consider the sampling variability from using consistent estimators to compute the shocks. We study the finite sample size of our tests in several simulation exercises, with special attention to resampling procedures. We also show that they have non-negligible power against a variety of empirically plausible alternatives.