Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment

Kim Christensen (), Ulrich Hounyo () and Mark Podolskij ()
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Kim Christensen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Ulrich Hounyo: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Mark Podolskij: Aarhus University and CREATES, Postal: Department of Mathematics, Ny Munkegade 118, 8000 Aarhus C, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump-diffusion process are available. The test is then based on asset returns, which are deflated by a model-free jump- and noise-robust estimate of the seasonal component and therefore homoscedastic under the null. The t-statistic (after pre-averaging and jump-truncation) diverges in the presence of stochastic volatility and has a standard normal distribution otherwise. We prove that replacing the true diurnal factor with our estimator does not affect the asymptotic theory. A Monte Carlo simulation also shows this substitution has no discernable impact in finite samples. The test is, however, distorted by small infinite-activity price jumps. To improve inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation in volatility, but important sources of heteroscedasticity remain present in the data.

Keywords: Bipower variation; bootstrapping; diurnal variation; high-frequency data; microstructure noise; pre-averaging; time-varying volatility (search for similar items in EconPapers)
JEL-codes: C10 C80 (search for similar items in EconPapers)
Pages: 62
Date: 2017-09-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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