Working Paper: NBER ID: w17152
Authors: Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
Abstract: We provide a first in-depth look at robust estimation of integrated quarticity (IQ) based on high frequency data. IQ is the key ingredient enabling inference about volatility and the presence of jumps in financial time series and is thus of considerable interest in applications. We document the significant empirical challenges for IQ estimation posed by commonly encountered data imperfections and set forth three complementary approaches for improving IQ based inference. First, we show that many common deviations from the jump diffusive null can be dealt with by a novel filtering scheme that generalizes truncation of individual returns to truncation of arbitrary functionals on return blocks. Second, we propose a new family of efficient robust neighborhood truncation (RNT) estimators for integrated power variation based on order statistics of a set of unbiased local power variation estimators on a block of returns. Third, we find that ratio-based inference, originally proposed by Barndorff-Nielsen and Shephard, has desirable robustness properties and is well suited for our empirical applications. We confirm that the proposed filtering scheme and the RNT estimators perform well in our extensive simulation designs and in an application to the individual Dow Jones 30 stocks.
Keywords: Integrated Quarticity; Volatility Estimation; High-Frequency Data
JEL Codes: G12; G13; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Filtering method (C38) | Accuracy of volatility measures (C58) |
| Robust neighborhood truncation estimators (C24) | Reliability of IQ estimates (C13) |
| Data imperfections (L15) | Reliability of volatility forecasts (G17) |
| Ratio-based inference method (C11) | Power of jump tests (C12) |
| Ratio-based inference method (C11) | Reliability of volatility forecasts (G17) |