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). (Intervento presentato al convegno COMPSTAT 2014 Conference Proceedings, International Conference on Computational Statistics, tenutosi a Geneva nel 19-22 August 2014).

A Generalized Description Length Approach for Sparse and Robust Index Tracking

Paterlini Sandra
2014-01-01

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
COMPSTAT 2014 Conference Proceedings, International Conference on Computational Statistics,
Heidelberg, Germany
Springer-Verlag
9781634395854
Giuzio, Margherita; Ferrari, Davide; Paterlini, Sandra
A Generalized Description Length Approach for Sparse and Robust Index Tracking / Giuzio, Margherita; Ferrari, Davide; Paterlini, Sandra. - (2014). (Intervento presentato al convegno COMPSTAT 2014 Conference Proceedings, International Conference on Computational Statistics, tenutosi a Geneva nel 19-22 August 2014).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/192928
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact