Heteroscedasticity test of high‐frequency data with jumps and market microstructure noise

Abstract

In this paper, we are interested in testing whether the volatility process is constant or not during a given time span by using high‐frequency data with the presence of jumps and market microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric procedure to depict the discrepancy between local variation and global variation. We show that our proposed test statistic converges to a standard normal distribution if the volatility is constant, and diverges to infinity otherwise. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to some real high‐frequency financial datasets. 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 in the periods near the opening and closing are highly volatile and account for a relatively large proportion of intraday variation.