Ulrich Hounyo

big data and econometrics seminar

Ulrich Hounyo

Aarhus University, CREATES
Testing for heteroscedasticity in jumpy and noisy high frequency data: A resampling approach
Co-écrit avec
Kim Christensen, Mark Podolskij
Lieu

VC Salle A

Centre de la Vieille-Charité - Salle A

Centre de la Vieille Charité
2 rue de la Charité
13002 Marseille

Date(s)
Mardi 7 mars 2017| 14:00 - 15:30
Contact(s)

Sébastien Laurent : sebastien.laurent[at]univ-amu.fr

Résumé

In this paper, we propose a new way to measure and test the presence of time-varying volatility in a discretely sampled jump-diffusion process that is contaminated by microstructure noise. We use the concept of pre-averaged truncated bipower variation to construct our t-statistic, which diverges in the presence of a heteroscedastic volatility term (and has a standard normal distribution otherwise). The test is inspected in a general Monte Carlo simulation setting, where we note that in finite samples the asymptotic theory is severely distorted by infinite-activity price jumps. To improve inference, we suggest a bootstrap approach to test the null of homoscedasticity. We prove the first-order validity of this procedure, while in simulations the bootstrap leads to almost correctly sized tests. As an illustration, we apply the bootstrapped version of our t-statistic to a large cross-section of equity high-frequency data. We document the importance of jump-robustness, when measuring heteroscedasticity in practice. We also find that a large fraction of variation in intraday volatility is accounted for by seasonality. This suggests that, once we control for jumps and deflate asset returns by a non-parametric estimate of the conventional U-shaped diurnality profile, the variance of the rescaled return series is often close to constant within the day.

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