Outlier detection based on Voronoi diagram for high frequency financial data

High frequency financial data possess unique features absent in data measured at lower frequencies, and analysis of these data poses interesting and unique challenges to econometric modeling and statistical analysis. The Traditional outlier detection method is based on statistical models, such as ARMA and ARCH, which require special hypotheses and are inappropriate to apply to high frequency data. This paper proposes a novel outlier detection method for high frequency financial data, which called Voronoi based Outlier Detection. Experiments show the new method performs effective in outlier detection for both daily and ultra-high-frequency financial data.