EconBase
← All papers

Heteroscedasticity test of high-frequency data with jumps and microstructure noise

Qiang Liu, Zhi Liu, Chuanhai Zhang

arXiv 15 Oct 2020 · Econometrics · publishedApplied Stochastic Models in Business and Industry (2022) · 1 citations (OpenAlex)

arXiv:2010.07659 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper, we are interested in testing if the volatility process is constant or not during a given time span by using high-frequency data with the presence of jumps and microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric way to depict the discrepancy between local variation and global variation. We show that our proposed test estimator converges to a standard normal distribution if the volatility is constant, otherwise it diverges to infinity. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to do the heteroscedasticity test for some real high-frequency financial data. We observe that in almost half of the days tested, the assumption of constant volatility within a day is violated. And this is due to that the stock prices during opening and closing periods are highly volatile and account for a relative large proportion of intraday variation.

Citation extraction

52
references
86
in-text mentions
52
distinct cited
3
self-citations
8,488
main-text words

appendix boundary found by appendix_titled_section at “Appendix” · 64% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Barndorff-Nielsen, O.E., Shephard, N (2004) Power and bipower variation with stochastic volatility and jumps0.92843100%
2Jing, B., Liu, Z., Kong, X (2014) On the estimation of integrated volatility with jumps and microstructure noise self0.81142100%
3Christensen, K., Hounyo, U., Podolskij, M (2018) Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment0.81142100%
4Mancini, C (2009) Nonparametric threshold estimation for models with stochastic diffusion coefficient and jumps0.7373367%
5Jacod, J (2008) Asymptotic properties of realized power variations and related functionals of semimartingales0.73732100%
6Jacod, J., Li, Y., Mykland, P.A., Podolskij, M., Vetter, M (2009) Microstructure noise in the continuous case: The pre-averaging approach0.73732100%
7Mancini, C., Renò, R (2011) Threshold estimation of markov models with jumps and interest rate modeling0.73732100%
8Aït-Sahalia, Y., Jacod, J (2009) Testing for jumps in a discretely observed process0.64422100%
9Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., Shephard, N (2008) Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise0.64422100%
10Barndorff-Nielsen, O.E., Shephard, N., Winkel, M (2006) Limit theorems for multipower variation in the presence of jumps0.64422100%

Showing the top 10 of 52 scored citations.