Mardi | 2010-06-08
B103

Bertrand MAILLET – P. MERLIN – libre

We propose a new methodology for abnormal return detection and correction, andevaluate the economic impacts of outliers on asset allocations with higher-order mo-ments (Cf. Maillet and Merlin, 2010). Indeed, extreme returns and outliers greatlya ect empirical higher-order moment estimations (Cf. Kim and White, 2004). We thusextend the outlier detection procedures of Franses and Ghijsels (1999) and Charles andDarn e (2005) with an Arti cial Neural Network – GARCH model (Cf. Donaldson andKamstra, 1997). The proposed method for deletion and correction of outliers, cou-pled with the use of a robust approach based on higher-order L-moments, clearly showsome improvements of the portfolio allocation performance in the French stock market.