Outliers Detection, Correction of Financial Time-Series Anomalies and Distributional Timing for Robust Efficient Higher-Order Moment Asset Allocations

We propose a new methodology for abnormal return detection and correction, and evaluate the economic impacts of outliers on asset allocations with higher-order moments (Cf. Jurczenko et al., 2008). Indeed, extreme returns and outliers greatly affect empirical higher-order moment estimations (Cf. Kim and White, 2004). We thus extend the outlier detection procedures of Franses and Ghijsels (1999) and Charles and Darne (2005) with an Artificial Neural Network - GARCH model (Cf. Donaldson and Kamstra, 1997). The proposed method for deletion and correction of outliers, coupled with the use of a robust approach based on higher-order L-moments, clearly show some improvements of the portfolio allocation performance in the French stock market