Outliers are ubiquitous in modern datasets. Due to noise, uncertainty, and adversarial
behavior, such observations are inherent to many real-world problems. In financial …

It is well-known that outliers exist in the type of multivariate data used by financial
practitioners for portfolio construction and risk management. Typically, outliers are …

Outliers represent a fundamental challenge in the empirical finance research. We
investigate whether the routine techniques used in finance research to identify and treat …

Financial asset returns and fundamental factor exposure data often contain outliers,
observations that are inconsistent with the majority of the data. Both academic finance …

Page 1 VOLUME 16 HOW TO DETECT AND HANDLE OUTLIERS by BORIS IGLEWICZ and
DAVID C. HOAGLIN AS American Society for Quality Control Statistics Division Page 2 Volume …

Outliers in the financial market data often carry important information, which requires
attention and investigation. Many outlier detection techniques, including both parametric and …

We employ a Boxplot method for detecting and analyzing outlying daily returns of 14
international stock market indices sampled from around the world. The main objective of the …

It is well-known that outliers exist in the type of multivariate data used by financial
practitioners to construct fundamental factor models of stock returns. Typically, outliers are …

Outlier is the observation that is not consistent with the rest of observations. It exists not only
in stock prices but also in the economic variables. In multifactor asset pricing model, the …

In this Master's thesis, different models for outlier detection in financial time series are
examined. The financial time series are price series such as index prices or asset prices …