We develop a new minimum description length criterion for index tracking, which deals with two main issues affecting portfolio weights: estimation errors and model misspecification. The criterion minimizes the uncertainty related to data distribution and model parameters by means of a generalized q-entropy measure, and performs model selection and estimation in a single step, by assuming a prior distribution on portfolio weights. The new approach results in sparse and robust portfolios in presence of outliers and high correlation, by penalizing observations and parameters that highly diverge from the assumed data model and prior distribution. The Monte Carlo simulations and the empirical study on financial data confirm the properties and the advantages of the proposed approach compared to state-of-art methods.

A Generalized Description Length Approach for Sparse and Robust Index Tracking / Giuzio, Margherita; Ferrari, Davide; Paterlini, Sandra. - (2014), pp. 157-165. (Intervento presentato al convegno 21st International Conference on Computational Statistics, COMPSTAT 2014 tenutosi a Geneva nel 19-22 August 2014).

A Generalized Description Length Approach for Sparse and Robust Index Tracking

PATERLINI, Sandra
2014

Abstract

We develop a new minimum description length criterion for index tracking, which deals with two main issues affecting portfolio weights: estimation errors and model misspecification. The criterion minimizes the uncertainty related to data distribution and model parameters by means of a generalized q-entropy measure, and performs model selection and estimation in a single step, by assuming a prior distribution on portfolio weights. The new approach results in sparse and robust portfolios in presence of outliers and high correlation, by penalizing observations and parameters that highly diverge from the assumed data model and prior distribution. The Monte Carlo simulations and the empirical study on financial data confirm the properties and the advantages of the proposed approach compared to state-of-art methods.
2014
21st International Conference on Computational Statistics, COMPSTAT 2014
Geneva
19-22 August 2014
157
165
Giuzio, Margherita; Ferrari, Davide; Paterlini, Sandra
A Generalized Description Length Approach for Sparse and Robust Index Tracking / Giuzio, Margherita; Ferrari, Davide; Paterlini, Sandra. - (2014), pp. 157-165. (Intervento presentato al convegno 21st International Conference on Computational Statistics, COMPSTAT 2014 tenutosi a Geneva nel 19-22 August 2014).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1143432
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