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

64 Pages Posted: 3 Jun 2009 Last revised: 21 Sep 2009

See all articles by Bertrand B. Maillet

Bertrand B. Maillet

EMLyon Business School (Paris Campus)

Paul M. Merlin

CES/CNRS - University of Paris-1 (Panthéon-Sorbonne); A.A.Advisors

Date Written: September 16, 2009

Abstract

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.

Keywords: Outliers, ANN-GARCH, Higher-order Moment, Asset Allocation

JEL Classification: C5, G1, G11, G12

Suggested Citation

Maillet, Bertrand B. and Merlin, Paul M., Outliers Detection, Correction of Financial Time-Series Anomalies and Distributional Timing for Robust Efficient Higher-Order Moment Asset Allocations (September 16, 2009). Available at SSRN: https://ssrn.com/abstract=1413623 or http://dx.doi.org/10.2139/ssrn.1413623

Bertrand B. Maillet

EMLyon Business School (Paris Campus) ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

Paul M. Merlin (Contact Author)

CES/CNRS - University of Paris-1 (Panthéon-Sorbonne) ( email )

Paris, IL
+33677960660 (Phone)

A.A.Advisors ( email )

3 av Hoche
Paris, 75008
France
+33677960660 (Phone)

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