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Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach

Ulrich Hounyo () and Rasmus T. Varneskov ()
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Ulrich Hounyo: University at Albany - State University of New York and CREATES, Postal: Department of Economics, University at Albany - State University of New York, Albany, NY 12222, USA
Rasmus T. Varneskov: Copenhagen Business School and CREATES, Postal: Department of Finance, Copenhagen Business School, 2000 Frederiksberg, Denmark

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

Abstract: We study inference for the local innovations of It^o semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the effect of "large" jumps. Our locally dependent wild bootstrap (LDWB) accommodate issues related to the stochastic scale and jumps as well as account for a special block-wise dependence structure induced by sampling errors. We show that the LDWB replicates first and second-order limit theory from the usual empirical process and the stochastic scale estimate, respectively, as well as an asymptotic bias. Moreover, we design the LDWB sufficiently general to establish asymptotic equivalence between it and and a nonparametric local block bootstrap, also introduced here, up to second-order distribution theory. Finally, we introduce LDWB-aided Kolmogorov-Smirnov tests for local Gaussianity as well as local von-Mises statistics, with and without bootstrap inference, and establish their asymptotic validity using the second-order distribution theory. The finite sample performance of CLT and LDWB-aided local Gaussianity tests are assessed in a simulation study as well as two empirical applications. Whereas the CLT test is oversized, even in large samples, the size of the LDWB tests are accurate, even in small samples. The empirical analysis verifies this pattern, in addition to providing new insights about the distributional properties of equity indices, commodities, exchange rates and popular macro finance variables.

Keywords: Bootstrap inference; High-frequency data; It^o semimartingales; Kolmogorov-Smirnov test; Stable processes; von-Mises statistics (search for similar items in EconPapers)
JEL-codes: C12 C14 C15 G1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
Date: 2018-04-26
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