SKEWNESS-CORRECTED COPULA-BASED OUTLIER DETECTION FOR HIGH-FREQUENCY FINANCIAL DATA FROM THE WARSAW STOCK EXCHANGE
SKEWNESS-CORRECTED COPULA-BASED OUTLIER DETECTION FOR HIGH-FREQUENCY FINANCIAL DATA FROM THE WARSAW STOCK EXCHANGE

Summary/Abstract: The objective of outlier detection is to identify rare events - the elements in data that have significantly different feature values than the rest of data. Thus, it is of high importance to conduct the outlier detection in financial data. We investigate the use of COPOD - a parameter-free anomaly detection algorithm based on application of empirical copulas and computation of the corresponding tail probabilities. Its performance is assessed on real-world data from the Warsaw Stock Exchange. We follow the theoretical framework of [Li et al. 2020] and restate its core formulas in a concise notation adapted later to the selected financial set.

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