THE EFFECT OF OUTLIERS ON THE PERFORMANCE OF AKAIKE INFORMATION CRITERION (AIC) AND BAYESIAN INFORMATION CRITERION (BIC) IN SELECTION OF AN ASYMMETRIC PRICE RELATIONSHIP

Asymmetric price transmission modelling aims to select one model that best captures the asymmetric data generating process from a set of competing models using model selection methods.However, such an interest in model selection outpace an awareness that outliers in data can have a disproportionate impact on model ranking.In order to explore the issue, the effect of outliers on the performance of commonly used Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) in selection of asymmetric price relationship are evaluated under conditions of different sample size.Monte Carlo experimentation indicated that the ability of the model selection methods to identify the true asymmetric price relationship decreases with an increase in outliers in moderate and large samples.With 5% outlier-contamination in large samples, both AIC and BIC fail to identify the true asymmetric price relationship.BIC outperforms AIC in selecting the asymmetric data generating process in large samples with outliers.However, in small samples, the effect of outliers on the performance of AIC and BIC in selection of the correct asymmetric model remains unclear.