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
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.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Barndorff-Nielsen, O.E., Shephard, N (2004) Power and bipower variation with stochastic volatility and jumps | 0.928 | 4 | 3 | 100% |
| 2 | Jing, B., Liu, Z., Kong, X (2014) On the estimation of integrated volatility with jumps and microstructure noise self | 0.811 | 4 | 2 | 100% |
| 3 | Christensen, K., Hounyo, U., Podolskij, M (2018) Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment | 0.811 | 4 | 2 | 100% |
| 4 | Mancini, C (2009) Nonparametric threshold estimation for models with stochastic diffusion coefficient and jumps | 0.737 | 3 | 3 | 67% |
| 5 | Jacod, J (2008) Asymptotic properties of realized power variations and related functionals of semimartingales | 0.737 | 3 | 2 | 100% |
| 6 | Jacod, J., Li, Y., Mykland, P.A., Podolskij, M., Vetter, M (2009) Microstructure noise in the continuous case: The pre-averaging approach | 0.737 | 3 | 2 | 100% |
| 7 | Mancini, C., Renò, R (2011) Threshold estimation of markov models with jumps and interest rate modeling | 0.737 | 3 | 2 | 100% |
| 8 | Aït-Sahalia, Y., Jacod, J (2009) Testing for jumps in a discretely observed process | 0.644 | 2 | 2 | 100% |
| 9 | Barndorff-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 noise | 0.644 | 2 | 2 | 100% |
| 10 | Barndorff-Nielsen, O.E., Shephard, N., Winkel, M (2006) Limit theorems for multipower variation in the presence of jumps | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.