Index tracking is a valuable low-cost alternative to active portfolio management. The implementation of a quantitative approach, however, is a major challenge from an optimization perspective. The optimal selection of a group of assets that can replicate the index of a much larger portfolio requires both to find the optimal subset of assets and to fine-tune their weights. The former is a combinatorial, the latter a continuous numerical problem. Both problems need to be addressed simultaneously, because whether or not a selection of assets is promising depends on the allocation weights and vice versa. Moreover, the problem is usually of high dimension. Typically, an optimal subset of 30-150 positions out of 100-600 need to be selected and their weights determined. Search heuristics can be a viable and valuable alternative to traditional methods, which often cannot deal with the problem. In this paper, we propose a new optimization method, which is partly based on Differential Evolution (DE) and on combinatorial search. The main advantage of our method is that it can tackle index tracking problem as complex as it is, generating accurate and robust results.

Krink, T., S., Mittnik e S., Paterlini. "Differential Evolution and Combinatorial Search for Constrained Index Traking" Working paper, CEFIN WORKING PAPERS, Dipartimento di Economia Marco Biagi - Università di Modena e Reggio Emilia, 2009. https://doi.org/10.25431/11380_1197333

Differential Evolution and Combinatorial Search for Constrained Index Traking

Krink, T.;Paterlini, S.
2009

Abstract

Index tracking is a valuable low-cost alternative to active portfolio management. The implementation of a quantitative approach, however, is a major challenge from an optimization perspective. The optimal selection of a group of assets that can replicate the index of a much larger portfolio requires both to find the optimal subset of assets and to fine-tune their weights. The former is a combinatorial, the latter a continuous numerical problem. Both problems need to be addressed simultaneously, because whether or not a selection of assets is promising depends on the allocation weights and vice versa. Moreover, the problem is usually of high dimension. Typically, an optimal subset of 30-150 positions out of 100-600 need to be selected and their weights determined. Search heuristics can be a viable and valuable alternative to traditional methods, which often cannot deal with the problem. In this paper, we propose a new optimization method, which is partly based on Differential Evolution (DE) and on combinatorial search. The main advantage of our method is that it can tackle index tracking problem as complex as it is, generating accurate and robust results.
2009
Marzo
Krink, T.; Mittnik, S.; Paterlini, S.
Krink, T., S., Mittnik e S., Paterlini. "Differential Evolution and Combinatorial Search for Constrained Index Traking" Working paper, CEFIN WORKING PAPERS, Dipartimento di Economia Marco Biagi - Università di Modena e Reggio Emilia, 2009. https://doi.org/10.25431/11380_1197333
File in questo prodotto:
File Dimensione Formato  
CEFIN-WP16.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 350.76 kB
Formato Adobe PDF
350.76 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1197333
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact