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Investment decisions and portfolios classifications based on robust methods of estimation

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  • Grażyna Trzpiot
  • Justyna Majewska

Abstract

In the process of assets selection and their allocation to the investment portfolio the most important factor issue thing is the accurate evaluation of the volatility of the return rate. In order to achieve stable and accurate estimates of parameters for contaminated multivariate normal distributions the robust estimators are required. In this paper we used some of the robust estimators to selection the optimal investment portfolios. The main goal of this paper was the comparative analysis of generated investment portfolios with respect to chosen robust estimation methods.

Suggested Citation

  • Grażyna Trzpiot & Justyna Majewska, 2008. "Investment decisions and portfolios classifications based on robust methods of estimation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 18(1), pages 83-96.
    • Handle: RePEc:wut:journl:v:1:y:2008:p:83-96
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    References listed on IDEAS

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    1. John Randal & Peter Thomson & Martin Lally, 2004. "Non-parametric estimation of historical volatility," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 427-440.
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    Cited by:

    1. Maria Cristina Arcuri & Gino Gandolfi & Fabrizio Laurini, 2023. "Robust portfolio optimization for banking foundations: a CVaR approach for asset allocation with mandatory constraints," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 557-581, June.
    2. Ewa Ratuszny, 2013. "Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(1), pages 35-63, March.

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