info:eu-repo/semantics/article
Optimal Portfolio Structuring in Emerging Stock Markets Using Robust Statistics
Optimal Portfolio Structuring in Emerging Stock Markets Using Robust Statistics
Autor
Reyna, Fernando R. Q.
Júnior, Antonio M. Duarte
Mendes, Beatriz V. M.
Porto, Oscar
Institución
Resumen
Emerging markets are known to have unique characteristics when compared to more developed markets. The direct use of standard mathematical models proposed and tested in more developed markets is not always recommended in emerging markets. Extreme events in emerging markets have already been verified to distort the results obtained when using standard mathematical models in several situations, including optimal portfolio structuring. Practitioners working in the asset management industry in emerging markets have not yet incorporated optimization models into their routine. One of the reasons for that is that extreme events and/or economic discontinuities (such as the Brazilian and the Argentinean devaluation crises etc.) modify the financial environment in such a way that past data become of little use when looking forward. In this article we concentrate on proposing a methodology to handle extreme events. Two numerical examples taken from the Brazilian stock market are used to illustrate the use of our proposal. Emerging markets are known to have unique characteristics when compared to more developed markets. The direct use of standard mathematical models proposed and tested in more developed markets is not always recommended in emerging markets. Extreme events in emerging markets have already been verified to distort the results obtained when using standard mathematical models in several situations, including optimal portfolio structuring. Practitioners working in the asset management industry in emerging markets have not yet incorporated optimization models into their routine. One of the reasons for that is that extreme events and/or economic discontinuities (such as the Brazilian and the Argentinean devaluation crises etc.) modify the financial environment in such a way that past data become of little use when looking forward. In this article we concentrate on proposing a methodology to handle extreme events. Two numerical examples taken from the Brazilian stock market are used to illustrate the use of our proposal.